Systems and methods for image reconstruction

ABSTRACT

The present disclosure provides a system for image reconstruction. The system may obtain an initial image of a subject. The initial image may be generated based on scan data of the subject that is collected by an imaging device. The system may also generate a gradient image associated with the initial image. The system may further generate a target image of the subject by applying an image reconstruction model based on the initial image and the gradient image. The target image may have a higher image quality than the initial image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority of Chinese Patent Application No.201811602836.3, filed on Dec. 26, 2018, the contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The disclosure generally relates to image processing, and moreparticularly, relates to systems and methods for image reconstruction.

BACKGROUND

Medical imaging, such as positron emission tomography (PET) and computedtomography (CT), is widely used in disease diagnosis and/or treatmentfor various medical conditions (e.g., tumors, coronary heart diseases,or brain disease). Image reconstruction is a key technology used in thefield of medical imaging to transform scan data of a subject (e.g., apatient) into an image of the subject. The image quality (e.g., measuredby an image resolution and/or a noise level) of the resulting image maybe affected by various factors, such as the dose of radiation applied tothe subject, the scan time, a motion of the subject during the scan, orthe like, or any combination thereof. In some occasions, in order toimprove the image quality of the resulting image, the scan of thesubject may need to be performed under a restricted condition, forexample, with a sufficiently high radiation dose, a sufficiently longscan time, the subject holding breath, or the like. However, this maycause more radiation damage to the subject and/or reduce the scanningefficiency. Merely by way of example, a PET scan with a standard PETdose may be performed at a speed of about 3 minutes/table position,while a PET scan with half of the standard PET dose may need to beperformed about 6 minutes/table position, which may increase the scantime and result in a patient movement in scan. Therefore, it is desiredto provide systems and methods for image reconstruction, therebyimproving the image quality, reducing the radiation damage, and/orimproving the scanning efficiency.

SUMMARY

According to one aspect of the present disclosure, a system for imagereconstruction is provided. The system may include at least one storagedevice including a set of instructions, and at least one processorconfigured to communicate with the at least one storage device. Whenexecuting the set of instructions, the at least one processor may beconfigured to direct the system to obtain an initial image of a subject.The initial image may be generated based on scan data of the subjectthat is collected by an imaging device. The at least one processor mayalso be configured to direct the system to generate a gradient imageassociated with the initial image. The at least one processor mayfurther be configured to direct the system to generate a target image ofthe subject by applying an image reconstruction model based on theinitial image and the gradient image. The target image may have a higherimage quality than the initial image.

In some embodiments, each of the initial image and the gradient imagemay be a 3-dimensional (3D) image. The generating a target image of thesubject based on the initial image and the gradient image by applying animage reconstruction model may include extracting at least one first2-dimensional (2D) image from the initial image, and extracting at leastone second 2D image from the gradient image. The generating a targetimage of the subject based on the initial image and the gradient imageby applying an image reconstruction model may further include generatingthe target image by applying the image reconstruction model based on theat least one first 2D image and the at least one second 2D image.

In some embodiments, the at least one first 2D image may include atleast one first axial image, at least one first sagittal image, and atleast one first coronary image extracted from the initial image. The atleast one second 2D image includes at least one second axial image, atleast one second sagittal image, and at least one second coronary imageextracted from the gradient image.

In some embodiments, the generating the target image based on the atleast one first 2D image and the at least one second 2D image byapplying the image reconstruction model may include generating a firstconcatenated image by concatenating the at least one first axial imageand the at least one second axial image, generating a secondconcatenated image by concatenating the at least one first sagittalimage and the at least one second sagittal image, and generating a thirdconcatenated image by concatenating the at least one first coronaryimage and the at least one second coronary image. The generating thetarget image based on the at least one first 2D image and the at leastone second 2D image by applying the image reconstruction model mayfurther include generating the target image by applying the imagereconstruction model on the first concatenated image, the secondconcatenated image, and the third concatenated image.

In some embodiments, the image reconstruction model may include an axialview component, a sagittal view component, a coronary view component,and an integration component. The axial view component may be configuredto generate a first feature map by processing the first concatenatedimage. The sagittal view component may be configured to generate asecond feature map by processing the second concatenated image. Thecoronary view component may be configured to generate a third featuremap by processing the third concatenated image. The integrationcomponent may be configured to generate an output image by processingthe first feature map, the second feature map, and the third featuremap. The target image may be generated based on the output image of theintegration component.

In some embodiments, the image reconstruction model may be a trainedcascaded neural network including a plurality of trained models that aresequentially connected. The plurality of trained models may include atrained first model and one or more trained second models downstream tothe trained first model. The generating the target image by applying theimage reconstruction model on the at least one first 2D image and the atleast one second 2D image may include obtaining an output image of thetrained first model by inputting the at least one first 2D image and theat least one second 2D image into the trained first model. For each ofthe one or more trained second model, the generating the target image byapplying the image reconstruction model on the at least one first 2Dimage and the at least one second 2D image may include extracting atleast one third 2D image from an output image of a previous trainedmodel connected to the trained second model, and obtaining an outputimage of the trained second model by inputting the at least one first 2Dimage, the at least one second 2D image, and the at least one third 2Dimage into the trained second model. The target image may be generatedbased on an output image of the last trained second model of the trainedcascaded neural network.

In some embodiments, the scan data of the initial image may correspondto a first radiation dose associated with the subject, and the targetimage may correspond to a second radiation dose higher than the firstradiation dose.

In some embodiments, the image reconstruction model may correspond to atarget image resolution. The initial image may have an image resolutiondifferent from the target image resolution. The at least one processormay be configured to direct the system to generate a resampled initialimage having the target image resolution by resampling the initialimage. The at least one processor may also be configured to direct thesystem to generate a preprocessed initial image by normalizing theresampled initial image, and generate a preprocessed gradient image bynormalizing the gradient image. The generating the target image of thesubject by applying the image reconstruction model based on the initialimage and the gradient image may include generating the target image ofthe subject by applying the image reconstruction model based on thepreprocessed initial image and the preprocessed gradient image.

In some embodiments, the image quality may relate to at least one of animage resolution, a noise level, a contrast ratio, or a sharpness.

In some embodiments, the image reconstruction model may be configured toreduce noise in the initial image.

In some embodiments, the image reconstruction model may include a neuralnetwork model.

According to another aspect of the present disclosure, a system forgenerating an image reconstruction model is provided. The system mayinclude at least one storage device including a set of instructions, andat least one processor configured to communicate with the at least onestorage device. When executing the set of instructions, the at least oneprocessor may be configured to direct the system to obtain a pluralityof training samples and a preliminary model. Each of the plurality oftraining samples may include a sample initial image of a sample subject,a sample gradient image associated with the sample initial image, and asample target image of the sample subject. The sample target image mayhave a higher image quality than the sample initial image. The at leastone processor may be configured to direct the system to generate theimage reconstruction model by training the preliminary model using theplurality of training sample.

In some embodiments, the generating the image reconstruction model bythe training the preliminary model using the plurality of trainingsamples may include, for each of the plurality of training samples,extracting at least one sample first 2D image from the sample initialimage of the training sample, and extracting at least one sample second2D image from the sample gradient image of the training sample. Thegenerating the image reconstruction model by the training thepreliminary model using the plurality of training samples may furtherinclude generating the image reconstruction model by training thepreliminary model using the at least one sample first 2D image, the atleast one sample second 2D image, and the sample target image of each ofthe plurality of training samples.

In some embodiments, for each of plurality of the training samples, theat least one sample first 2D image may include at least one sample firstaxial image, at least one sample first sagittal image, and at least onesample first coronary image extracted from the sample initial image ofthe training sample. For each of plurality of the training samples, theat least one sample second 2D image may include at least one samplesecond axial image, at least one sample second sagittal image, and atleast one sample second coronary image extracted from the samplegradient image of the training sample.

In some embodiments, the training the preliminary model may include, foreach of the plurality of training samples, generating a sample firstconcatenated image by concatenating the at least one sample first axialimage and the at least one sample second axial image of the trainingsample, generating a sample second concatenated image by concatenatingthe at least one sample first sagittal image and the at least one samplesecond sagittal image of the training sample, and generating a samplethird concatenated image by concatenating the at least one sample firstcoronary image and the at least one sample second coronary image of thetraining sample. The training the preliminary model may further includetraining the preliminary model using the sample first concatenatedimage, the sample second concatenated image, the sample thirdconcatenated image, and the sample target image of each of the pluralityof training samples.

In some embodiments, the preliminary model may include a firstcomponent, a second component, a third component, and a fourthcomponent. The first component may be configured to generate a samplefirst feature map by processing the sample first concatenated image ofeach of the plurality of training samples. The second component may beconfigured to generate a sample second feature map by processing thesample second concatenated image of each of the plurality of trainingsamples. The third component may be configured to generate a samplethird feature map by processing the sample third concatenated image ofeach of the plurality of training samples. The fourth component may beconfigured to process the sample first feature map, the sample secondfeature map, and the sample third feature map of each of the pluralityof training samples.

In some embodiments, the preliminary model may be a cascaded neuralnetwork including a plurality of models that are sequentially trained.The plurality of models may include a first model and one or more secondmodels downstream to the first model. The training the preliminary modelmay include, for each of the plurality of training samples, training thefirst model using the at least one sample first 2D image, the at leastone sample second 2D image, and the sample target image of the trainingsample. For each of the one or more second models, training thepreliminary model may include, for each of the plurality of trainingsamples, obtaining a sample output image by inputting the at least onesample first 2D image and the at least one sample second 2D image of thetraining sample into the one or more trained models that are generatedbefore training the second model, and extracting at least one samplethird 2D image from the sample output image corresponding to thetraining sample. For each of the one or more second models, training thepreliminary model may include, for each of the plurality of trainingsamples, training the second model using the at least one sample first2D image, the at least one sample second 2D image, the at least onesample third 2D image, and the sample target image of the trainingsample.

In some embodiments, for each of the plurality of training samples, thecorresponding sample initial image may be generated based on first scandata that is collected by a sample imaging device and correspond to afirst radiation dose associated with the sample subject. For each of theplurality of training samples, the corresponding sample target image maybe generated based on second scan data that is collected by the sampleimaging device and correspond to a second radiation dose associated withthe sample subject. For each of the plurality of training samples, thesecond radiation dose may be higher than the first radiation dose.

In some embodiments, the image quality may relate to at least one of animage resolution, a noise level, a contrast ratio, or a sharpness.

In some embodiments, the generating the image reconstruction model bytraining the preliminary model using the plurality of training samplesmay include preprocessing the plurality of training samples, andgenerating the image reconstruction model based on the plurality ofpreprocessing training samples.

In some embodiments, the image reconstruction model may include a neuralnetwork model.

In some embodiments, the image reconstruction model may include at leastone of a fully convolutional block, a skip-connection, a residual block,or a dense block.

According to another aspect of the present disclosure, a method forimage reconstruction is provided. The method may include obtaining aninitial image of a subject, the initial image being generated based onscan data of the subject that is collected by an imaging device. Themethod may also include generating a gradient image associated with theinitial image. The method may further include generating a target imageof the subject by applying an image reconstruction model based on theinitial image and the gradient image. The target image having a higherimage quality than the initial image.

According to another aspect of the present disclosure, a method forgenerating an image reconstruction model is provided. The method mayinclude obtaining a plurality of training samples and a preliminarymodel. Each of the plurality of training samples may include a sampleinitial image of a sample subject, a sample gradient image associatedwith the sample initial image, and a sample target image of the samplesubject. The sample target image may have a higher image quality thanthe sample initial image. The method may further include generating theimage reconstruction model by training the preliminary model using theplurality of training samples.

According to another aspect of the present disclosure, a non-transitorycomputer-readable storage medium including instructions is provided.When accessed by at least one processor of a system for imagereconstruction, the instructions may cause the system to perform amethod. The method may include obtaining an initial image of a subject,the initial image being generated based on scan data of the subject thatis collected by an imaging device. The method may also includegenerating a gradient image associated with the initial image. Themethod may further include generating a target image of the subject byapplying an image reconstruction model based on the initial image andthe gradient image. The target image having a higher image quality thanthe initial image.

According to another aspect of the present disclosure, a non-transitorycomputer-readable storage medium including instructions is provided.When accessed by at least one processor of a system for generating animage reconstruction model, the instructions may cause the system toperform a method. The method may include obtaining a plurality oftraining samples and a preliminary model. Each of the plurality oftraining samples may include a sample initial image of a sample subject,a sample gradient image associated with the sample initial image, and asample target image of the sample subject. The sample target image mayhave a higher image quality than the sample initial image. The methodmay further include generating the image reconstruction model bytraining the preliminary model using the plurality of training samples.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device may be implemented accordingto some embodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device according to some embodimentsof the present disclosure;

FIGS. 4A and 4B are block diagrams illustrating exemplary processingdevices according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for generating atarget image of a subject based on an initial image of a subjectaccording to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for generating atarget image of a subject by applying an image reconstruction modelaccording to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for generating atarget image of a subject by applying a trained cascaded neural networkaccording to some embodiments of the present disclosure;

FIG. 8 is a schematic diagram illustrating an exemplary model accordingto some embodiments of the present disclosure;

FIG. 9 is a schematic diagram illustrating an exemplary trained cascadedneural network according to some embodiments of the present disclosure;

FIG. 10 is a flowchart illustrating an exemplary process for generatingan image reconstruction model according to some embodiments of thepresent disclosure;

FIG. 11 is a flowchart illustrating an exemplary process for training apreliminary model using a plurality of training samples according tosome embodiments of the present disclosure;

FIG. 12 is a flowchart illustrating an exemplary process for training acascaded neural network according to some embodiments of the presentdisclosure;

FIG. 13A is a schematic diagram illustrating an exemplary sample initialimage according to some embodiments of the present disclosure;

FIG. 13B is a schematic diagram illustrating an exemplary predictedtarget image according to some embodiments of the present disclosure;

FIG. 13C is a schematic diagram illustrating an exemplary sample targetimage according to some embodiments of the present disclosure;

FIG. 14A is a schematic diagram illustrating an exemplary sample initialimage according to some embodiments of the present disclosure;

FIG. 14B is a schematic diagram illustrating an exemplary predictedtarget image according to some embodiments of the present disclosure;

FIG. 14C is a schematic diagram illustrating an exemplary sample targetimage according to some embodiments of the present disclosure;

FIG. 15A is a schematic diagram illustrating an exemplary sample initialimage according to some embodiments of the present disclosure;

FIG. 15B is a schematic diagram illustrating an exemplary predictedtarget image according to some embodiments of the present disclosure;and

FIG. 15C is a schematic diagram illustrating an exemplary sample targetimage according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections or assembly of differentlevels in ascending order. However, the terms may be displaced byanother expression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items. The term “image” in the present disclosure isused to collectively refer to image data (e.g., scan data) and/or imagesof various forms, including a two-dimensional (2D) image, athree-dimensional (3D) image, a four-dimensional (4D) image, etc. Theterm “pixel” and “voxel” in the present disclosure are usedinterchangeably to refer to an element of an image.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

Moreover, while the systems and methods disclosed in the presentdisclosure are described primarily regarding image reconstruction in amedical imaging system. It should be understood that this is only forillustration purposes. The systems and methods of the present disclosuremay be applied to reconstruct image data acquired in different scenariosand/or for different purposes (e.g., safety monitoring, filming, orphotography) and/or by different image acquisition devices (e.g., adigital camera, an analog camera, or a scanner).

For example, the systems and methods of the present disclosure may beapplied to various imaging systems. In some embodiments, the imagingsystems may include a single modality imaging system and/or amulti-modality imaging system. The single modality imaging system mayinclude, for example, an ultrasound imaging system, an X-ray imagingsystem, an computed tomography (CT) system, a magnetic resonance imaging(MRI) system, an ultrasonography system, a positron emission tomography(PET) system, an optical coherence tomography (OCT) imaging system, anultrasound (US) imaging system, an intravascular ultrasound (IVUS)imaging system, a near infrared spectroscopy (NIRS) imaging system, orthe like, or any combination thereof. The multi-modality imaging systemmay include, for example, an X-ray imaging-magnetic resonance imaging(X-ray-MRI) system, a positron emission tomography-X-ray imaging(PET-X-ray) system, a single photon emission computedtomography-magnetic resonance imaging (SPECT-MRI) system, a positronemission tomography-computed tomography (PET-CT) system, a C-arm system,a digital subtraction angiography-magnetic resonance imaging (DSA-MRI)system, etc. It should be noted that the imaging system described belowis merely provided for illustration purposes, and not intended to limitthe scope of the present disclosure.

The term “imaging modality” or “modality” as used herein broadly refersto an imaging method or technology that gathers, generates, processes,and/or analyzes imaging information of a subject. The subject mayinclude a biological subject and/or a non-biological subject. Thebiological subject may be a human being, an animal, a plant, or aportion thereof (e.g., a cell, a tissue, an organ, etc.). In someembodiments, the subject may be a man-made composition of organic and/orinorganic matters that are with or without life.

An aspect of the present disclosure relates to systems and methods forimage reconstruction. The systems and methods may acquire an initialimage, wherein the initial image may be generated based on scan data ofthe subject that is collected by an imaging device. The systems andmethods may generate a gradient image associated with the initial image.Based on the initial image and the gradient image, the systems andmethods may generate a target image of the subject by application of animage reconstruction model. The target image may have a higher imagequality than the initial image. For example, the target image may have ahigher image resolution and/or a lower noise level than the initialimage.

According to some embodiments of the present disclosure, the targetimage may be reconstructed based on the initial image with a relativelylower image quality by applying the image reconstruction model.Normally, an image of a same (or substantially same) quality as thetarget image may need to be generated based on scan data collected in amore restricted condition than the initial image (e.g., with a higherradiation dose, a longer scan time, the subject holding breath). Thismay cause more radiation damage to the subject and/or reduce thescanning efficiency. The systems and methods disclosed herein may beused to generate an image with improved quality without increasingradiation damage to the subject and/or without reducing the scanningefficiency.

In addition, in some embodiments, the image reconstruction model may bea neural network model that is trained using a machine learningtechnique. Using the image reconstruction model may further improve thereconstruction efficiency and/or the accuracy of the reconstructionresult. Moreover, the gradient image associated with the initial imagemay be generated and used in reconstructing the target image. Thegradient image may provide detail information (e.g., edge information)of the initial image, which may facilitate the recovery of image detailsof the initial image in generating the target image.

In some embodiments, the systems and methods may extract multi-viewinformation (e.g., information in an axial view, a sagittal view, and/ora coronary view) from the initial image and the gradient image. Theimage reconstruction model may include multiple components forprocessing the multi-view information of the initial image and thegradient image. The target image reconstructed based on the multi-viewinformation of the initial image and the gradient image may have ahigher accuracy and/or reliability. Additionally or alternatively, insome embodiments, the image reconstruction model may be a trainedcascaded neural network, and the reliability of the target image may befurther improved by adopting a deep auto-context learning strategy.

FIG. 1 is a schematic diagram illustrating an exemplary imaging system100 according to some embodiments of the present disclosure. In someembodiments, the imaging system 100 may be a single-modality system(e.g., a PET system, a CT system) or a multi-modality system (e.g., aPET-CT system, a PET/MRI system) as described elsewhere in thisdisclosure. In some embodiments, the imaging system 100 may includemodules and/or components for performing imaging and/or relatedanalysis.

Merely by way of example, as illustrated in FIG. 1, the imaging system100 may include an imaging device 110, a network 120, one or moreterminals 130, a processing device 140, and a storage device 150. Thecomponents in the imaging system 100 may be connected in various ways.Merely by way of example, the imaging device 110 may be connected to theprocessing device 140 through the network 120 or directly as illustratedin FIG. 1. As another example, the terminal(s) 130 may be connected tothe processing device 140 via the network 120 or directly as illustratedin FIG. 1.

The imaging device 110 may be configured to acquire imaging datarelating to a subject. The imaging data relating to a subject mayinclude a 2D image (e.g., a slice image), a 3D image, a 4D image (e.g.,a series of 3D images over time), and/or any related image data (e.g.,scan data, projection data), or the like. The subject may be biologicalor non-biological. For example, the subject may include a patient, aman-made object, etc. As another example, the subject may include aspecific portion, organ, and/or tissue of the patient. For example, thesubject may include the head, the neck, the thorax, the heart, thestomach, a blood vessel, soft tissue, a tumor, nodules, or the like, orany combination thereof.

In some embodiments, the imaging device 110 may include a PET device.The PET device may scan the subject or a portion thereof that is locatedwithin its detection region and generate projection data relating to thesubject or the portion thereof. The PET device may include a gantry, adetector, an electronics module, and/or other components not shown. Thegantry may support one or more parts of the PET device, for example, thedetector, the electronics module, and/or other components. The detectormay detect radiation photons (e.g., y photons) emitted from the subjectbeing examined. The electronics module may collect and/or processelectrical signals (e.g., scintillation pulses) generated by thedetector. The electronics module may convert an analog signal (e.g., anelectrical signal generated by the detector) relating to a radiationphoton detected by the detector to a digital signal relating to aradiation event. As used herein, a radiation event may refer to aninteraction between a radiation photon emitted from a subject andimpinging on and detected by the detector. A pair of radiation photons(e.g., y photons) interacting with two detector blocks along a line ofresponse (LOR) within a coincidence time window may be determined as acoincidence event. A portion of the radiation photons (e.g., y photons)emitted from a subject being examined may interact with tissue in thesubject. The radiation photons (e.g., y photons) interacting with tissuein the subject may be scattered or otherwise change its trajectory, thatmay affect the number or count of radiation photons (e.g., y photons)detected by two detector blocks along a line of response (LOR) within acoincidence time window and the number or count of coincidence events.In some alternative embodiments, the imaging device 110 may include aPET/CT device or a PET/MRI device.

The processing device 140 may process data and/or information obtainedfrom the imaging device 110, the terminal(s) 130, and/or the storagedevice 150. For example, the processing device 140 may reconstruct atarget image of the subject by applying an image reconstruction model.As another example, the processing device 140 may generate the imagereconstruction model by training a preliminary model using a pluralityof training samples. In some embodiments, the generation and/or updatingof the image reconstruction model may be performed on a processingdevice, while the application of the image reconstruction model may beperformed on a different processing device. In some embodiments, thegeneration of the image reconstruction model may be performed on aprocessing device of a system different from the imaging system 100 or aserver different from a server including the processing device 140 onwhich the application of the image reconstruction model is performed.For instance, the generation of the image reconstruction model may beperformed on a first system of a vendor who provides and/or maintainssuch an image reconstruction model and/or has access to training samplesused to generate the image reconstruction model, while imagereconstruction based on the provided image reconstruction model may beperformed on a second system of a client of the vendor. In someembodiments, the generation of the image reconstruction model may beperformed online in response to a request for image reconstruction. Insome embodiments, the generation of the image reconstruction model maybe performed offline.

In some embodiments, the image reconstruction model may be generatedand/or updated (or maintained) by, e.g., the manufacturer of the imagingdevice 110 or a vendor. For instance, the manufacturer or the vendor mayload the image reconstruction model into the imaging system 100 or aportion thereof (e.g., the processing device 140) before or during theinstallation of the imaging device 110 and/or the processing device 140,and maintain or update the image reconstruction model from time to time(periodically or not). The maintenance or update may be achieved byinstalling a program stored on a storage device (e.g., a compact disc, aUSB drive, etc.) or retrieved from an external source (e.g., a servermaintained by the manufacturer or vendor) via the network 120. Theprogram may include a new model (e.g., a new image reconstruction model)or a portion of a model that substitute or supplement a correspondingportion of the model.

In some embodiments, the processing device 140 may be a computer, a userconsole, a single server or a server group, etc. The server group may becentralized or distributed. In some embodiments, the processing device140 may be local or remote. For example, the processing device 140 mayaccess information and/or data stored in the imaging device 110, theterminal(s) 130, and/or the storage device 150 via the network 120. Asanother example, the processing device 140 may be directly connected tothe imaging device 110, the terminal(s) 130 and/or the storage device150 to access stored information and/or data. In some embodiments, theprocessing device 140 may be implemented on a cloud platform. Merely byway of example, the cloud platform may include a private cloud, a publiccloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof.

The storage device 150 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 150 may store dataobtained from the terminal(s) 130 and/or the processing device 140. Forexample, the storage device 150 may store image data (e.g., a PET image)acquired by the imaging device 110. As another example, the storagedevice 150 may store one or more algorithms for processing the imagedata, an image reconstruction model for image reconstruction, etc. Insome embodiments, the storage device 150 may store data and/orinstructions that the processing device 140 may execute or use toperform exemplary methods/systems described in the present disclosure.In some embodiments, the storage device 150 may include a mass storagedevice, a removable storage device, a volatile read-and-write memory, aread-only memory (ROM), or the like, or any combination thereof.Exemplary mass storage devices may include a magnetic disk, an opticaldisk, a solid-state drive, etc. Exemplary removable storage devices mayinclude a flash drive, a floppy disk, an optical disk, a memory card, azip disk, a magnetic tape, etc. Exemplary volatile read-and-writememories may include a random access memory (RAM). Exemplary RAM mayinclude a dynamic RAM (DRAM), a double date rate synchronous dynamic RAM(DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. Exemplary ROM may include a mask ROM(MROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM),an electrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage device 150 may be implemented on a cloud platform. Merely byway of example, the cloud platform may include a private cloud, a publiccloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more other components in theimaging system 100 (e.g., the processing device 140, the terminal(s)130, etc.). One or more components in the imaging system 100 may accessthe data or instructions stored in the storage device 150 via thenetwork 120. In some embodiments, the storage device 150 may be directlyconnected to or communicate with one or more other components in theimaging system 100 (e.g., the processing device 140, the terminal(s)130, etc.). In some embodiments, the storage device 150 may be part ofthe processing device 140 or the imaging device 110.

The terminal(s) 130 may include a mobile device 131, a tablet computer132, a laptop computer 133, or the like, or any combination thereof. Insome embodiments, the mobile device 131 may include a smart home device,a wearable device, a mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof. Insome embodiments, the smart home device may include a smart lightingdevice, a control device of an intelligent electrical apparatus, a smartmonitoring device, a smart television, a smart video camera, aninterphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a bracelet, a footgear,eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory,or the like, or any combination thereof. In some embodiments, the mobiledevice may include a mobile phone, a personal digital assistant (PDA), agaming device, a navigation device, a point of sale (POS) device, alaptop, a tablet computer, a desktop, or the like, or any combinationthereof. In some embodiments, the virtual reality device and/or theaugmented reality device may include a virtual reality helmet, virtualreality glasses, a virtual reality patch, an augmented reality helmet,augmented reality glasses, an augmented reality patch, or the like, orany combination thereof. For example, the virtual reality device and/orthe augmented reality device may include a Google Glass™, an OculusRift™, a Hololens™, a Gear VR™, etc. In some embodiments, theterminal(s) 130 may be part of the processing device 140 or the imagingdevice 110.

The network 120 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the imaging device 110 (e.g., aCT device, a PET device, etc.), the terminal(s) 130, the processingdevice 140, the storage device 150, etc., may communicate informationand/or data with one or more other components of the imaging system 100via the network 120. For example, the processing device 140 may obtainimage data from the imaging device 110 via the network 120. As anotherexample, the processing device 140 may obtain user instructions from theterminal(s) 130 via the network 120.

The network 120 may be and/or include a public network (e.g., theInternet), a private network (e.g., a local area network (LAN), a widearea network (WAN)), etc.), a wired network (e.g., an Ethernet network),a wireless network (e.g., an 802.11 network, a Wi-Fi network, etc.), acellular network (e.g., a Long Term Evolution (LTE) network), a framerelay network, a virtual private network (“VPN”), a satellite network, atelephone network, routers, hubs, switches, server computers, and/or anycombination thereof. Merely by way of example, the network 120 mayinclude a cable network, a wireline network, a fiber-optic network, atelecommunications network, an intranet, a wireless local area network(WLAN), a metropolitan area network (MAN), a public telephone switchednetwork (PSTN), a Bluetooth™ network, a ZigBee™ network, a near fieldcommunication (NFC) network, or the like, or any combination thereof. Insome embodiments, the network 120 may include one or more network accesspoints. For example, the network 120 may include wired and/or wirelessnetwork access points such as base stations and/or internet exchangepoints through which one or more components of the imaging system 100may be connected to the network 120 to exchange data and/or information.

It should be noted that the above description of the imaging system 100is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, the imagingsystem 100 may include one or more additional components and/or one ormore components of the imaging system 100 described above may beomitted. Additionally or alternatively, two or more components of theimaging system 100 may be integrated into a single component. Acomponent of the imaging system 100 may be implemented on two or moresub-components.

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device 200 may be implementedaccording to some embodiments of the present disclosure. The computingdevice 200 may be used to implement any component of the imaging systemas described herein. For example, the processing device 140 and/or aterminal 130 may be implemented on the computing device 200,respectively, via its hardware, software program, firmware, or acombination thereof. Although only one such computing device is shown,for convenience, the computer functions relating to the imaging system100 as described herein may be implemented in a distributed fashion on anumber of similar platforms, to distribute the processing load. Asillustrated in FIG. 2, the computing device 200 may include a processor210, a storage 220, an input/output (I/O) 230, and a communication port240.

The processor 210 may execute computer instructions (program codes) andperform functions of the processing device 140 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, signals, datastructures, procedures, modules, and functions, which perform particularfunctions described herein. For example, the processor 210 mayreconstruct a target image of a subject based on an initial image of thesubject, wherein the target image may have a higher image quality thanthe initial image (e.g., a lower noise level and/or a higher imageresolution). As another example, the processor 210 may generate an imagereconstruction model according to a machine learning technique. In someembodiments, the processor 210 may perform instructions obtained fromthe terminal(s) 130. In some embodiments, the processor 210 may includeone or more hardware processors, such as a microcontroller, amicroprocessor, a reduced instruction set computer (RISC), anapplication-specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field-programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), any circuit orprocessor capable of executing one or more functions, or the like, orany combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors. Thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 220 may store data/information obtained from the imagingdevice 110, the terminal(s) 130, the storage device 150, or any othercomponent of the imaging system 100. In some embodiments, the storage220 may include a mass storage device, a removable storage device, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. In some embodiments, the storage 220 maystore one or more programs and/or instructions to perform exemplarymethods described in the present disclosure. For example, the storage220 may store a program for the processing device 140 to reconstruct atarget image of a subject.

The I/O 230 may input or output signals, data, and/or information. Insome embodiments, the I/O 230 may enable user interaction with theprocessing device 140. In some embodiments, the I/O 230 may include aninput device and an output device. Exemplary input devices may include akeyboard, a mouse, a touch screen, a microphone, or the like, or acombination thereof. Exemplary output devices may include a displaydevice, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Exemplary display devices may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), or the like, or a combination thereof.

The communication port 240 may be connected with a network (e.g., thenetwork 120) to facilitate data communications. The communication port240 may establish connections between the processing device 140 and theimaging device 110, the terminal(s) 130, or the storage device 150. Theconnection may be a wired connection, a wireless connection, or acombination of both that enables data transmission and reception. Thewired connection may include an electrical cable, an optical cable, atelephone wire, or the like, or any combination thereof. The wirelessconnection may include a Bluetooth network, a Wi-Fi network, a WiMaxnetwork, a WLAN, a ZigBee network, a mobile network (e.g., 3G, 4G, 5G,etc.), or the like, or any combination thereof. In some embodiments, thecommunication port 240 may be a standardized communication port, such asRS232, RS485, etc. In some embodiments, the communication port 240 maybe a specially designed communication port. For example, thecommunication port 240 may be designed in accordance with the digitalimaging and communications in medicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device 300 according to someembodiments of the present disclosure. In some embodiments, one or morecomponents (e.g., a terminal 130 and/or the processing device 140) ofthe imaging system 100 may be implemented on the mobile device 300.

As illustrated in FIG. 3, the mobile device 300 may include acommunication platform 310, a display 320, a graphics processing unit(GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory360, and a storage 390. In some embodiments, any other suitablecomponent, including but not limited to a system bus or a controller(not shown), may also be included in the mobile device 300. In someembodiments, a mobile operating system 370 (e.g., iOS, Android, WindowsPhone, etc.) and one or more applications 380 may be loaded into thememory 360 from the storage 390 in order to be executed by the CPU 340.The applications 380 may include a browser or any other suitable mobileapps for receiving and rendering information relating to imageprocessing or other information from the processing device 140. Userinteractions with the information stream may be achieved via the I/O 350and provided to the processing device 140 and/or other components of theimaging system 100 via the network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. The hardware elements, operating systems and programminglanguages of such computers are conventional in nature, and it ispresumed that those skilled in the art are adequately familiar therewithto adapt those technologies to generate an image as described herein. Acomputer with user interface elements may be used to implement apersonal computer (PC) or another type of work station or terminaldevice, although a computer may also act as a server if appropriatelyprogrammed. It is believed that those skilled in the art are familiarwith the structure, programming and general operation of such computerequipment and as a result, the drawings should be self-explanatory.

FIG. 4A and FIG. 4B are block diagrams illustrating exemplary processingdevices 140A and 140B according to some embodiments of the presentdisclosure. In some embodiments, the processing devices 140A and 140Bmay be embodiments of the processing device 140 as described inconnection with FIG. 1. In some embodiments, the processing devices 140Aand 140B may be respectively implemented on a processing unit (e.g., theprocessor 210 illustrated in FIG. 2 or the CPU 340 as illustrated inFIG. 3). Merely by way of example, the processing devices 140A may beimplemented on a CPU 340 of a terminal device, and the processing device140B may be implemented on a computing device 200. As another example,the processing device 140A may be implemented on a computing device ofthe imaging system 100, while the processing device 140B may be part ofa device or system of the manufacturer of the imaging system 100, or aportion thereof (e.g., the imaging device 110), or a vendor thatmaintains the imaging system 100, or a portion thereof (e.g., theimaging device 110). Alternatively, the processing devices 140A and 140Bmay be implemented on a same computing device 200 or a same CPU 340. Forexample, the processing devices 140A and 140B may be implemented on asame computing device 200.

As shown in FIG. 4A, the processing device 140A may include anacquisition module 401, a gradient image generation module 402, and atarget image generation module 403.

The acquisition module 401 may be configured to obtain an initial imageof a subject (e.g., a patient). An initial image of the subject refersto an image that is generated based on scan data of the subject, whereinthe scan data may be collected by an imaging device during a scan of thesubject. In some embodiments, the initial image may correspond to afirst radiation dose. The acquisition module 401 may obtain the initialimage from the imaging device or a storage device (e.g., the storagedevice 150, the storage 220, an external source) that stores the initialimage. More descriptions regarding the initial image may be foundelsewhere in the present disclosure. See, e.g., operation 510 andrelevant descriptions thereof.

The gradient image generation module 402 may be configured to generate agradient image associated with the initial image. A gradient imageassociated with the initial image is used herein to collectively referto any image that includes detail information (e.g., edge information,texture information, color information) of the initial image. In someembodiments, the gradient image generation module 402 may generate thegradient image by processing the initial image using a gradient operator(e.g., a Sobel operator) or a guided-filtering algorithm. Moredescriptions regarding the generation of the gradient image may be foundelsewhere in the present disclosure. See, e.g., operation 520 andrelevant descriptions thereof.

The target image generation module 403 may be configured to generate atarget image of the subject by applying an image reconstruction modelbased on the initial image and the gradient image. A target image of thesubject refers to an image that is reconstructed based on the initialimage by applying an image reconstruction model, wherein the targetimage may have a higher image quality than the initial image. An imagereconstruction model refers to a model configured to output an imagethat has a desired image quality based on its input. The imagereconstruction model may be of any type of neural network model, such asa trained cascaded neural network. In some embodiments, the target imagemay correspond to a second radiation dose higher than the firstradiation dose. More descriptions regarding the generation of the targetimage may be found elsewhere in the present disclosure. See, e.g.,operation 530 and relevant descriptions thereof.

As shown in FIG. 4B, the processing device 140B may include anacquisition module 404 and a model generation module 405.

The acquisition module 404 may be configured to obtain a plurality oftraining samples and a preliminary model. Each of the raining samplesmay include a sample initial image of a sample subject, a samplegradient image associated with the sample initial image, and a sampletarget image of the sample subject. The sample target image of eachtraining sample may have a higher image quality than the sample initialimage of the training sample. More descriptions regarding the trainingsamples may be found elsewhere in the present disclosure. See, e.g.,operation 1110 and relevant descriptions thereof. The preliminary modelmay be of any type of neural network model, for example, a neuralnetwork model (e.g., a CNN model, a GAN model, a cascaded neuralnetwork, or the like). More descriptions regarding the preliminary modelmay be found elsewhere in the present disclosure. See, e.g., operation1120 and relevant descriptions thereof.

The model generation module 405 may be configured to generate the imagereconstruction model by training the preliminary model using theplurality of training samples. In some embodiments, the model generationmodule 405 may train the preliminary model according to a machinelearning algorithm as described elsewhere in this disclosure (e.g., FIG.5 and the relevant descriptions). Merely by way of example, the modelgeneration module 405 may generate the image reconstruction modelaccording to a supervised machine learning algorithm by performing oneor more iterations to iteratively update the model parameter(s) of thepreliminary model. More descriptions regarding the generation of theimage reconstruction model may be found elsewhere in the presentdisclosure. See, e.g., operation 1130 and relevant descriptions thereof.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.Each of the modules described above may be a hardware circuit that isdesigned to perform certain actions, e.g., according to a set ofinstructions stored in one or more storage media, and/or any combinationof the hardware circuit and the one or more storage media.

In some embodiments, the processing device 140A and/or the processingdevice 140B may share two or more of the modules, and any one of themodules may be divided into two or more units. For instance, theprocessing devices 140A and 140B may share a same acquisition module,that is, the acquisition module 401 and the acquisition module 404 are asame module. In some embodiments, the processing device 140A and/or theprocessing device 140B may include one or more additional modules, suchas a storage module (not shown) for storing data. In some embodiments,the processing device 140A and the processing device 140B may beintegrated into one processing device 140.

FIG. 5 is a flowchart illustrating an exemplary process 500 forgenerating a target image of a subject based on an initial image of thesubject according to some embodiments of the present disclosure. As usedherein, a subject may be biological or non-biological. For example, thesubject may include a patient (or a portion thereof), a man-made object,etc., as described elsewhere in the present disclosure (e.g., FIG. 1 andthe descriptions thereof). An initial image of the subject refers to animage that is generated based on scan data of the subject, wherein thescan data may be collected by an imaging device during a scan of thesubject. A target image of the subject refers to an image that isreconstructed based on the initial image by applying an imagereconstruction model, wherein the target image may have a higher imagequality than the initial image.

The image quality of an image may be measured by one or more imagequality indexes, such as an image resolution, a noise level, a contrastratio, a sharpness value, or the like, or any combination thereof. Theimage quality of two images may be compared by comparing the one or moreimage quality indexes. For example, the target image may be regarded ashaving a higher image quality than the initial image if the imageresolution of the target image may be higher than that of the initialimage. Additionally or alternatively, the target image may be regardedas having a higher image quality than the initial image if the targetimage may have a lower noise level (e.g., fewer artifacts) than theinitial image.

In some embodiments, the initial image may correspond to a firstradiation dose. The first radiation dose refers to the dose of radiationapplied to or received by the subject in collecting the scan datacorresponding to the initial image. For example, the initial image maybe a PET image generated based on PET data relating to the subject. Thefirst radiation dose may be measured by the amount of radioactive tracer(e.g., fludeoxy glucose, prostate specific membrane antigen, etc.)injected into the subject before the collection of the PET data. Asanother example, the initial image may be a CT image generated based onCT data relating to the subject. The first radiation dose may bemeasured by the amount of X-rays emitted by an X-ray source incollecting the CT data.

The target image may correspond to a second radiation dose higher thanthe first radiation dose. As aforementioned, the target image may bereconstructed based on the initial image and the image reconstructionmodel without performing an actual scan on the subject. The secondradiation dose refers to a predicted or simulated dose of radiation thatneeds to be applied to or received by the subject to collect scan datafor generating an image of a same (or substantially same) quality as thetarget image. In other words, to generate a certain image that is of thesame (or substantially same) quality as the target image by performingan actual scan on the subject, the radiation dose applied to or receivedby the subject in the actual scan may be substantially equal to thesecond radiation dose. Normally, the radiation dose applied to orreceived by the subject in a scan may have an effect on a resultingimage of the scan. For example, if other imaging conditions remainunchanged, increasing the radiation dose may result in an image having ahigher image quality (e.g., a lower noise level and/or a higher imageresolution). However, increasing the radiation dose may cause moreradiation and/or damage to the subject. The systems and methodsdisclosed herein may be used to generate the target image with animproved image quality without increasing the radiation and damage tothe subject. In addition, in some embodiments, the systems and methodsdisclosed herein may improve the scan efficiency by reducing the scantime and/or reducing a probability of patient movement in scan.

In some embodiments, each of the first radiation dose and the secondradiation dose may be represented in the form of a number or a numberrange. The first radiation dose and/or the second radiation dose may beset according to a defaulting setting of the imaging system 100.Alternatively, the first radiation dose and/or the second radiation dosemay be set manually by a user or a computing device (e.g., theprocessing device 140A) according to an actual need. In someembodiments, the second radiation dose may be a standard dose commonlyused in generating images of the same type as the target image (e.g., astandard dose in PET imaging).

It should be noted that the above description regarding the initialimage and the target image is merely provided for the purposes ofillustration, and not intended to limit the scope of the presentdisclosure. The difference of radiation dose may be an exemplary factorthat induces the difference of image quality between the initial imageand the target image. One or more other factors, such as, a scan time, ahardware performance of the imaging device, a patient movement, may alsoaffect the image quality of the initial image. The systems and methodsmay be applied to reduce or eliminate the effect of one or more otherfactors to improve the image quality. Merely by way of example, theinitial image may include respiratory artifacts caused by a respiratorymotion of the subject in collecting the scan data corresponding to theinitial image. By performing the process 500, a target image with lessrespiratory artifacts may be generated based on the initial image.

In some embodiments, process 500 may be implemented as a set ofinstructions (e.g., an application) stored in a storage device (e.g.,the storage device 150, the storage 220, and/or the storage 390). Theprocessing device 140A (e.g., the processor 210, the CPU 340, and/or oneor more modules illustrated in FIG. 4A) may execute the set ofinstructions, and when executing the instructions, the processing device140A may be configured to perform the process 500. The operations of theillustrated process presented below are intended to be illustrative. Insome embodiments, the process 500 may be accomplished with one or moreadditional operations not described and/or without one or more of theoperations discussed. Additionally, the order of the operations ofprocess 500 illustrated in FIG. 5 and described below is not intended tobe limiting.

In 510, the processing device 140A (e.g., the acquisition module 401)may obtain the initial image of the subject.

As described above, the initial image may be generated based on the scandata of the subject collected by the imaging device. The initial imagemay be a 2D image (e.g., a slice image), a 3D image, a 4D image (e.g., aseries of 3D images over time), or any other type of image. For example,the initial image may be a 3D PET image generated based on scan datacollected by, such as, a PET device, a PET/CT device, or a PET/MRIdevice. In some embodiments, the initial image may be obtained from theimaging device or a storage device that stores the initial image (e.g.,the storage device 150, the storage 220, the storage 390, or an externalsource). For example, the initial image may be obtained from a medicalimage database, such as a Picture Archiving and Communication System(PACS), via a network (e.g., the network 120).

In some alternative embodiments, the processing device 140A may acquirethe scan data from the imaging device or a storage device (e.g., thestorage device 150, the storage 220, an external source) that stores thescan data. The processing device 140A may further reconstruct theinitial image based on the scan data according to an imagereconstruction algorithm. For example, the processing device 140A mayobtain scan data of the subject from a PET device or a PET/CT device,and reconstruct a PET image the initial image based on the scan dataaccording to a PET image reconstruction algorithm. Exemplary PET imagereconstruction algorithms may include an ordered subset expectationmaximization (OSEM) algorithm, a filtered back projection (FBP)algorithm, a maximum-likelihood reconstruction of attenuation andactivity (MLAA) algorithm, or the like, or any combination thereof.

Optionally, the processing device 140A may preprocess the initial image.The preprocessing of the initial image may include one or more imageprocessing operations, such as an image denoising, an image enhancement,an image smoothing, an image transformation, an image resampling, animage normalization, or the like, or a combination thereof. In someembodiments, the preprocessing of the initial image may include an imageresampling and an image normalization, which may be performedsimultaneously or in any sequence.

Merely by way of example, the image reconstruction model may correspondto a target image resolution, for example, be trained by training imageshaving the target image resolution. The processing device 140A maydetermine whether the image resolution of the initial image is the same(or substantially same) as the target image resolution. If the initialimage has an imaging resolution different from the target imageresolution, the processing device 140A may resample the initial image togenerate a resampled initial image having the target image resolution.Merely by way of example, the target image resolution may be 100 pixelsper inch (PPI), and the image resolution of the initial image may be 80PPI or 120 PPI. The processing device 140A may generate a resampledinitial image with an image resolution of 100 PPI according to an imageresampling algorithm. Exemplary image resampling algorithms may includea nearest neighbor algorithm, a bilinear interpolation algorithm, acubic convolution algorithm, a bilinear and bicubic algorithm, a Sincand Lanczos resampling algorithm, a box sampling algorithm, a mipmapalgorithm, a Fourier-transform algorithm, an edge-directed interpolationalgorithm, a vectorization algorithm, a deep convolution neural network,or the like, or any combination thereof.

The processing device 140A may further generate a preprocessed initialimage by normalizing the resampled initial image. In some embodiments,the resampled initial image may be normalized such that the preprocessedinitial image may have a preset format. For example, pixel (or voxel)values of the preprocessed initial image may be within a preset range(e.g., [−1, 1]). In some embodiments, the resampled initial image may benormalized according to Equation (1) as below:

$\begin{matrix}{{I^{\prime} = \frac{I - I_{\min}}{I_{\max} - I_{\min}}},} & (1)\end{matrix}$where I may represent the resampled initial image or, I′ may representthe preprocessed initial image, I_(max) may represent the maximum pixelvalue (or voxel value) of the resampled initial image, and I_(min) mayrepresent the minimum pixel value (or voxel value) of the resampledinitial image.

If the imaging resolution of the initial image is equal to the targetimage resolution, the resampling of the initial image may be omitted,and the processing device 140A may generate the preprocessed initialimage by normalizing the initial image. It should be noted that theabove description of preprocessing of the initial image is merelyprovided for the purposes of illustration, and not intended to limit thescope of the present disclosure. For example, the processing device 140Amay normalize the initial image, and then resample the normalizedinitial image to generate the preprocessed initial image. In someembodiments, the preprocessed initial image may be previously generatedby the processing device 140A (or another computing device) and storedin a storage device. The processing device 140A may directly obtain thepreprocessed initial image from the storage device, and designate thepreprocessed initial image as the initial image.

In 520, the processing device 140A (e.g., the gradient image generationmodule 402) may generate a gradient image associated with the initialimage.

A gradient image associated with the initial image is used herein tocollectively refer to any image that includes detail information (e.g.,edge information, texture information, color information) of the initialimage. For example, the gradient image may indicate a variation of pixelvalue (or voxel value) in the initial image. In some embodiments, thegradient image may be a 2D image or a 3D image. Using the gradient imagein reconstructing the target image may facilitate the recovery of imagedetails of the initial image, thereby generating a target image withimproved image quality.

In some embodiments, the processing device 140A may generate thegradient image by processing the initial image using a gradientoperator. Exemplary gradient operators may include a Sobel operator, aPrewit operator, a Robert cross operator, a Laplacian operator, a Scharroperator, or the like, or any combination thereof. As another example,the processing device 140A may generate an image that includes detailinformation of the initial image by processing the initial imageaccording to a guided-filtering algorithm. Merely by way of example, theprocessing device 140A may filter the initial image, and divide theinitial image by the filtered initial image to generate the gradientimage. For the convenience of descriptions, an image that includesdetail information of the initial image generated by theguided-filtering algorithm or another algorithm is referred to as agradient image herein.

In some embodiments, the initial image may need to be preprocessed asdescribed in connection with operation 510. The processing device 140Amay generate the gradient image by processing the preprocessed initialimage using, for example, a gradient operator or a guided-filteringalgorithm.

Optionally, the processing device 140A may generate a preprocessedgradient image by normalizing the gradient image. For example, thegradient image may be normalized according to Equation (2) as below:

$\begin{matrix}{{G^{\prime} = \frac{G - G_{\min}}{G_{\max} - G_{\min}}},} & (2)\end{matrix}$where G may represent the gradient image, G′ may represent thepreprocessed gradient image, G_(max) may represent the maximum pixelvalue (or voxel value) of the gradient image, and G_(min) may representthe minimum pixel value (or voxel value) of the gradient image.

In 530, the processing device 140A (e.g., the target image generationmodule 403) may generate the target image of the subject by applying animage reconstruction model based on the initial image and the gradientimage.

As used herein, an image reconstruction model refers to a modelconfigured to output an image that has a desired image quality based onits input. For example, the input of the image reconstruction model maybe the initial image and the gradient image. As another example, theinitial image and the gradient image may be preprocessed as described inconnection with operations 510 and 520. The input of the imagereconstruction model may be the preprocessed initial image and thepreprocessed gradient image. In some embodiments, an input of the imagereconstruction model may include a plurality of images. The images maybe directly inputted into the image reconstruction model, or beconcatenated into one or more concatenated images and inputted into themodel. For the convenience of descriptions, the following descriptionsare described with reference to generating the target image based on aninput including the initial image and the gradient image, and notintended to limit the scope of the present disclosure.

In some embodiments, the processing device 140A may extract one or morefirst 2D images from the initial image and one or more second 2D imagesfrom the gradient image. The processing device 140A may further generatethe target image based on the first 2D image(s) and the second 2Dimage(s) by applying the image reconstruction model. Optionally, thefirst 2D image(s) may include multi-view information (e.g., informationin an axial view, a sagittal view, and/or a coronary view) of theinitial image, and the second 2D image(s) may include multi-viewinformation of the gradient image. Using the multi-view information ofthe initial image and the gradient image may improve the accuracy of thereconstruction result. More descriptions regarding the first and second2D images may be found elsewhere in the present disclosure. See, e.g.,FIG. 6 and relevant descriptions thereof.

The image reconstruction model may be of any type of neural networkmodel. For example, the image reconstruction model may include a neuralnetwork model, such as a convolutional neural network (CNN) model (e.g.,a full CNN model, V-net model, a U-net model, an AlexNet model, anOxford Visual Geometry Group (VGG) model, a ResNet model), a generativeadversarial network (GAN) model, or the like, or any combinationthereof. Optionally, the image reconstruction model may include one ormore components for feature extraction and/or feature combination, suchas a fully convolutional block, a skip-connection, a residual block, adense block, or the like, or any combination thereof.

In some embodiments, the image reconstruction model may include multiplecomponents (e.g., an axial view component 810, a sagittal view component820, a coronary view component 830, and an integration component 840 asshown in FIG. 8) configured to process multi-view information of theinitial image and the gradient image. More descriptions regarding themultiple components may be found elsewhere in the present disclosure.See, e.g., FIGS. 6 and 8 and relevant descriptions thereof.

In some embodiments, the image reconstruction model may be a trainedcascaded neural network including a plurality of trained models that aresequentially connected. The plurality of trained models may include atrained first model and at least one trained second model downstream tothe trained first model. The trained first model may be configured toprocess an input of the trained cascaded neural network, and eachtrained second model may be configured to process an output of apreviously trained model connected to the trained second model.Optionally, the trained cascaded neural network may adopt a deepauto-context learning strategy, according to which the input of thetrained cascaded neural network may be also inputted into each trainedsecond model. This may avoid a loss of image data due to operations(e.g., a convolutional operation) performed during the application ofthe trained cascaded neural network. More descriptions regarding thetrained cascaded neural network may be found elsewhere in the presentdisclosure. See, e.g., FIGS. 7 and 9 and relevant descriptions thereof.

In some embodiments, the processing device 140A (e.g., the acquisitionmodule 401) may obtain the image reconstruction model from one or morecomponents of the imaging system 100 (e.g., the storage device 150, theterminals(s) 130) or an external source via a network (e.g., the network120). For example, the image reconstruction model may be previouslytrained by a computing device (e.g., the processing device 140B), andstored in a storage device (e.g., the storage device 150, the storage220, and/or the storage 390) of the imaging system 100. The processingdevice 140A may access the storage device and retrieve the imagereconstruction model. In some embodiments, the image reconstructionmodel may be generated according to a machine learning algorithm. Themachine learning algorithm may include but not be limited to anartificial neural network algorithm, a deep learning algorithm, adecision tree algorithm, an association rule algorithm, an inductivelogic programming algorithm, a support vector machine algorithm, aclustering algorithm, a Bayesian network algorithm, a reinforcementlearning algorithm, a representation learning algorithm, a similarityand metric learning algorithm, a sparse dictionary learning algorithm, agenetic algorithm, a rule-based machine learning algorithm, or the like,or any combination thereof. The machine learning algorithm used togenerate the image reconstruction model may be a supervised learningalgorithm, a semi-supervised learning algorithm, an unsupervisedlearning algorithm, or the like. In some embodiments, the imagereconstruction model may be generated by a computing device (e.g., theprocessing device 140B) by performing a process (e.g., process 1000) forgenerating an image reconstruction model disclosed herein. Moredescriptions regarding the generation of the image reconstruction modelmay be found elsewhere in the present disclosure. See, e.g., FIGS. 10-12and relevant descriptions thereof.

In some embodiments, the image reconstruction model may directly outputthe target image of the subject according to its input. Alternatively,the image reconstruction model may output an initial target image, andthe processing device 140A may need to post-process the initial targetimage of the subject to generate the target image. Merely by way ofexample, if the image resolution of the initial image is equal to thetarget image resolution, the preprocessing of the initial image may beperformed without resampling the initial image. The initial image may benormalized before the application of the image reconstruction modelaccording to its maximum pixel value (or voxel value) (denoted asI′_(max)) and its minimum pixel value (or voxel value) (denoted asI′_(min)). The processing device 140A may generate the target image byperforming a denormalization operation on the initial target image. Forexample, the initial target image may be denormalized according toEquation (3) as below:T′=T*(I′ _(max) −I′ _(min))+I′ _(min),  (3)where T may represent the initial target image, and T′ may represent thetarget image. In some embodiments, if the image resolution of theinitial image is not equal to the target image resolution, thepreprocessing of the initial image may include image normalization andimage resampling. The processing device 140A may further resample thedenormalized target image to generate a final target image that has thesame image resolution as the original initial image.

It should be noted that the above description regarding the process 500is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations of the process500 may be omitted and/or one or more additional operations may beadded.

For example, a storing operation may be added elsewhere in the process500. In the storing operation, the processing device 140A may storeinformation and/or data (e.g., the target image, the imagereconstruction model, etc.) associated with the imaging system 100 in astorage device (e.g., the storage device 150) disclosed elsewhere in thepresent disclosure. As another example, a first additional operation maybe performed between operations 510 and 520 to preprocess the initialimage, and a second additional operation may be performed betweenoperations 520 and 530 to preprocess the gradient image. In 530, thetarget image may be generated based on the preprocessed initial imageand the preprocessed gradient image by applying the image reconstructionmodel. As yet another example, after 530, the processing device 140A maytransmit the target image of the subject to a terminal (e.g., a terminal130) for display.

In some embodiments, the process 500 may be used in testing the imagereconstruction model. Merely by way of example, after the imagereconstruction model is generated, a computing device (e.g., theprocessing device 140A or 140B) may test the image reconstruction modelusing a set of testing samples. Each testing sample may include atesting initial image of a testing subject and a known target image ofthe testing subject. For example, the testing initial image of a testingsubject may be generated based on scan data of the testing subjectcorresponding to the first radiation dose. The known target image of atesting subject may be generated based on scan data of the testingsubject corresponding to the second radiation dose. For each testingsample, the computing device may perform process 500 on the testinginitial image of the testing sample to generate a corresponding targetimage. The computing device may further compare the target image and theknown target image of each testing sample. The comparison result (e.g.,a difference between the target image and the known target image of eachtesting sample) may indicate an accuracy and/or a reliability of theimage reconstruction model. The computing device may evaluate the imagereconstruction model based on the comparison result.

FIG. 6 is a flowchart illustrating an exemplary process for generating atarget image of a subject by applying an image reconstruction modelaccording to some embodiments of the present disclosure. In someembodiments, process 600 may be implemented as a set of instructions(e.g., an application) stored in a storage device (e.g., the storagedevice 150, storage 220, and/or storage 390). The processing device 140A(e.g., the processor 210, the CPU 340, and/or one or more modulesillustrated in FIG. 4A) may execute the set of instructions, and whenexecuting the instructions, the processing device 140A may be configuredto perform the process 600. The operations of the illustrated processpresented below are intended to be illustrative. In some embodiments,one or more operations of the process 600 may be performed to achieve atleast part of operation 530 as described in connection with FIG. 5.

In 610, the processing device 140A (e.g., the target image generationmodule 403) may extract one or more first 2D images from the initialimage.

In some embodiments, the initial image may be a 3D image, and a first 2Dimage may be extracted from the 3D initial image along any direction.For example, the first 2D image(s) may include one or more first axialimages, one or more first sagittal images, and one or more first coronalimages. As used herein, an axial image of a subject refers to an imagecorresponding to an axial view of the subject; a sagittal image refersto an image corresponding to a sagittal view of the subject; and acoronal image refers to an image corresponding to a coronal view of thesubject. The count of the first 2D image(s) may be equal to any positiveinteger, such as, 1, 3, 5, 10, 15, or the like.

In 620, the processing device 140A (e.g., the target image generationmodule 403) may extract one or more second 2D images from the gradientimage.

In some embodiments, the gradient image may be a 3D image, and a second2D image may be extracted from the 3D gradient image along anydirection. For example, the second 2D image(s) may include one or moresecond axial images, one or more second sagittal images, and one or moresecond coronal images. The count of the second 2D image(s) may be equalto any positive integer, such as, 1, 3, 5, 10, 15, or the like.

In some embodiments, the count of the second 2D image(s) may be equal tothat of the first 2D image(s). Each of the second 2D image(s) maycorrespond to one of the first 2D image(s). As used herein, a second 2Dimage and a first 2D image may be regarded as corresponding to eachother if they correspond to a same view (e.g., an axial view, a sagittalview, a coronal view) of a same portion (e.g., a same cross-section) ofthe subject.

In 630, based on the first 2D image(s) and the second 2D image(s), theprocessing device 140A (e.g., the target image generation module 403)may generate the target image of the subject by applying the imagereconstruction model.

For example, the processing device 140A may input the first 2D image(s)and the second 2D image(s) into the image reconstruction model. Theimage reconstruction model may output the target image. Alternatively,the image reconstruction model may output an initial target image, andthe processing device 140A may post-process the initial target image togenerate the target image. Optionally, the processing device 140A mayconcatenate the first 2D image(s) and the second 2D image(s) into one ormore concatenated images, and input the one or more concatenated imagesinto the image reconstruction model. The image reconstruction model mayoutput the target image or the initial target image in response to theone or more concatenated images.

Merely by way of example, the first 2D image(s) may include the firstaxial image(s), the first sagittal image(s), and the first coronaryimage(s) extracted from the initial image. The second 2D image(s) mayinclude the second axial image(s), the second sagittal image(s), and thesecond coronary image(s) extracted from the gradient image. Theprocessing device 140A may generate a first concatenated image byconcatenating the first axial image(s) and the second axial image(s); asecond concatenated image by concatenating by the first sagittalimage(s) and the second sagittal image(s); and a third concatenatedimage by concatenating by the first coronary image(s) and the secondcoronary image(s).

For illustration purposes, the generation of the first concatenatedimage is described as an example. The first axial image(s) and thesecond axial image(s) may be concatenated along a preset dimension(e.g., a channel dimension). For example, each of the first axialimage(s) and the second axial image(s) may both be a 2-dimensional imageincluding a first dimension and a second dimension. The first axialimage(s) and the second axial image(s) may be concatenated along a thirddimension to generate the first concatenated image (e.g., a3-dimensional image including the first, second, and third dimensions).Merely by way of example, 5 first axial images and 5 second axialimages, each of which has an image resolution of 64*64, may beconcatenated to generate a first 3D concatenated image having an imageresolution of 64*64*10. In some embodiments, the concentration of aplurality of 2D images along a channel dimension may also be referred toas a 2.5D concatenation.

The first, second, and third concatenated images may then be inputtedinto the image reconstruction model for processing. In some embodiments,the image reconstruction model may have a same or similar configurationas a trained model 800 as shown in FIG. 8. As illustrated in FIG. 8, thetrained model 800 may include an axial view component 810, a sagittalview component 820, a coronary view component 830, and an integrationcomponent 840. The first, second, and third concatenated images mayserve as an input 850, an input 860, and an input 870, respectively, ofthe trained model 800. The axial view component 810 may be configured toreceive the first concatenated image (i.e., the input 850), and generatea first feature map by processing the first concatenated image. Thesagittal view component 820 may be configured to receive the secondconcatenated image (i.e., the input 860), and generate a second featuremap by processing the second concatenated image. The coronary viewcomponent 830 may be configured to receive the third concatenated image(i.e., the input 870), and generate a third feature map by processingthe third concatenated image. The integration component 840 may beconfigured to generate an output image (e.g., the target image or theinitial target image) based on the first feature map, the second featuremap, and the third feature map. Optionally, the first, second, and thirdfeature maps may be concatenated into a fourth concatenated image beforebeing inputted into the integration component 840.

The types of the axial view component 810, the sagittal view component820, the coronary view component 830, and the integration component 840may be the same as or different from each other. For example, themultiple components of the trained model 800 may all be convolutionalnetworks. Optionally, a component of the trained model 800 may includeat least one of a fully convolutional block, a skip-connection, aresidual block, or a dense block. The first 2D image(s) and the second2D image(s) may include multi-view information of the initial image andthe gradient image, respectively. The multiple components of the trainedmodel 800 may extract features of the initial image and the gradientimage from different views, which may improve the accuracy of thereconstruction result.

In some embodiments, the image reconstruction model may be a trainedcascaded neural network that includes a plurality of sequentiallyconnected models. The processing device 140A may input the first 2Dimage(s) and the second 2D image(s) (e.g., in the form of first, second,and third concatenated images) into the trained cascaded neural networkto generate an output (e.g., the target image or the initial targetimage). For example, the processing device 140A may perform one or moreoperations of process 700 as described in connection with FIG. 7 togenerate the target image by applying the trained cascaded neuralnetwork.

It should be noted that the above description regarding the process 600is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. The operations of the illustrated process presented beloware intended to be illustrative. In some embodiments, the process 600may be accomplished with one or more additional operations not describedand/or without one or more of the operations discussed. For example,operations 610 and 620 may be omitted. In 630, the processing device140A may generate a concatenated image by concatenating the initialimage (or preprocessed initial image) and the gradient image (orpreprocessed initial image), and generate the target image based on theconcatenated image. Additionally, the order of the operations of process600 illustrated in FIG. 6 and described below is not intended to belimiting. For example, operations 610 and 620 may be performedsimultaneously, or operation 620 may be performed before operation 610.

FIG. 7 is a flowchart illustrating an exemplary process for generating atarget image of a subject by applying a trained cascaded neural networkaccording to some embodiments of the present disclosure. In someembodiments, process 700 may be implemented as a set of instructions(e.g., an application) stored in a storage device (e.g., the storagedevice 150, storage 220, and/or storage 390). The processing device 140A(e.g., the processor 210, the CPU 340, and/or one or more modulesillustrated in FIG. 4A) may execute the set of instructions, and whenexecuting the instructions, the processing device 140A may be configuredto perform the process 700. The operations of the illustrated processpresented below are intended to be illustrative. In some embodiments,the process 700 may be accomplished with one or more additionaloperations not described and/or without one or more of the operationsdiscussed. Additionally, the order of the operations of process 700illustrated in FIG. 7 and described below is not intended to belimiting. In some embodiments, one or more operations of the process 700may be performed to achieve at least part of operation 630 as describedin connection with FIG. 6

The trained cascaded neural network may be an exemplary embodiment ofthe image reconstruction model as described elsewhere in this disclosure(e.g., FIGS. 5 and 6 and the relevant descriptions). The trainedcascaded neural network may include a plurality of trained models thatare sequentially connected. The plurality of trained models may includea trained first model and one or more trained second models downstreamto the trained first model. A trained model of the trained cascadedneural network may be of any type of models, such as a CNN model, a GANmodel, or the like. The plurality of trained models of the trainedcascaded neural network may be of the same type or different types.

In 710, the processing device 140A (e.g., the target image generationmodule 403) may obtain an output image of the trained first model byinputting the first 2D image(s) and the second 2D image(s) into thetrained first model.

In some embodiments, the processing device 140A may directly input thefirst 2D image(s) and the second 2D image(s) into the trained cascadedneural network. Alternatively, the processing device 140A may generateone or more concatenated images (e.g., the first, second, and thirdconcatenated images as described in connection with operation 630) byconcatenating the first 2D image(s) and the second 2D image(s). Theprocessing device 140A may further input the one or more concatenatedimages into the trained cascaded neural network. In some embodiments,the trained first model may have a same or similar configuration as thetrained model 800 as shown in FIG. 8.

In 720, for each of the trained second model(s), the processing device140A (e.g., the target image generation module 403) may extract one ormore third 2D image from an output image of a previous trained modelthat is upstream and connected to the trained second model.

For example, for an i^(th) trained second model, the processing device140A may extract third 2D image(s) from an output image of an (i−1)^(th)trained model of the trained cascaded neural network. The output imageof the (i−1)^(th) trained model may be a 3D image, and a third 2D imagemay extracted from the output image of the (i−1)^(th) trained modelalong any direction. For example, the third 2D image(s) may include oneor more third axial images, one or more third sagittal images, and oneor more third coronal images extracted from the output image of the(i−1)^(th) trained model.

In some embodiments, for each trained second model, the count of thethird 2D image(s) of the may be equal to that of the first 2D image(s)(or the second 2D image(s)). Each of the third 2D image(s) maycorrespond to one of the first 2D image(s) and one of the second 2Dimage(s). As used herein, a first 2D image, a second 2D image, and athird 2D image may be regarded as corresponding to each other if theycorrespond to a same view (the axial view, the sagittal view, thecoronal view) of a same portion (e.g., a same cross-section) of thesubject.

In 730, for each of the trained second model(s), the processing device140A (e.g., the target image generation module 403) may obtain an outputimage of the trained second model by inputting the first 2D image(s),the second 2D image(s), and the third 2D image (s) into the trainedsecond model. The output image of the last trained second model of thetrained cascaded neural network may be designated as an output of thetrained cascaded neural network (e.g., the target image or the initialtarget image as described elsewhere in this disclosure).

For example, for the i^(th) trained second model, the processing device140A may obtain the corresponding output image by directly inputting thefirst 2D image(s), the second 2D image(s), and the third 2D image (s)into the i^(th) trained second model. Alternatively, the processingdevice 140A may generate a fifth concatenated image by concatenating thefirst axial image(s), the second axial image(s), and the third axialimage(s); a sixth concatenated image by concatenating by the firstsagittal image(s), the second sagittal image(s), and third sagittalimage(s); and a seventh concatenated image by concatenating by the firstcoronary image(s), the second coronary image(s), and the third coronaryimage(s). The processing device 140A may obtain the corresponding outputimage by further inputting the fifth, sixth, and seventh concatenatedimages into the i^(th) trained second model.

It should be noted that the above description regarding process 700 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations may be added oromitted. In some embodiments, in 710, the input of the trained firstmodel may be the (preprocessed) initial image and the (preprocessed)gradient image. Operation 710 may be omitted. In 730, the input of eachtrained second model may be the output image of a previous trained modelthat is upstream and connected to the trained second model andoptionally the input of the trained first model. In some embodiments, in730, the input of each trained second model may merely include theoutput image (or the third 2D image(s)) of the previous trained modelconnected to the trained second model.

FIG. 9 is a schematic diagram illustrating an exemplary trained cascadedneural network 900 according to some embodiments of the presentdisclosure. In some embodiments, the trained cascaded neural network 900may be an exemplary embodiment of an image reconstruction model asdescribed elsewhere in this disclosure (e.g., FIG. 5 and the relevantdescriptions). The processing device 140A may use the trained cascadedneural network 900 to reconstruct a target image of a subject based onan initial image of the subject and optionally a gradient imageassociated with the initial image.

As shown in FIG. 9, the trained cascaded neural network 900 may includea trained first model 902 and a plurality of trained second models(e.g., 907, 908) downstream to the trained first model 902. Theprocessing device 140A may obtain an input 901 of the trained cascadedneural network 900, and input the input 901 into the trained first model902. For example, the input 901 may include the initial image and thecorresponding gradient image (or a concatenated image of the initialimage and the corresponding gradient image). As another example, theinput 901 may include one or more first 2D images and one or more second2D images. The first 2D image(s) may include one or more first axialimages, one or more first sagittal images, and one or more firstcoronary images extracted from the initial image. The second 2D image(s)may include one or more second axial images, one or more second sagittalimages, and one or more second coronary images extracted from thegradient image. As yet another example, the input 901 may include afirst concatenated image, a second concatenated image, and a thirdconcatenated image generated based on the first 2D image(s) and thesecond 2D image(s).

The trained first model 902 may be configured to process the input 901and generate an output image 903. In some embodiments, the trained firstmodel 902 may have a same or similar configuration as the trained model800 as shown in FIG. 8. The output image 903 may be generated by anintegration component of the trained first model 902.

The processing device 140A may then generate an input of the trainedsecond model 907 based on the output image 903. For example, the inputof the trained second model 907 may be the output image 903. As anotherexample, the input of the trained second model 907 may be a combinationof the output image 903 and the input 901 as shown in FIG. 9. In someembodiments, the processing device 140A may generate a concatenatedimage of the output image 903 and the input 901 as the input of thetrained second model 907.

For example, the input 901 may include the first and second axialimages, the first and second sagittal images, and the first and secondcoronary images. The processing device 140A may extract one or morethird axial images, one or more third sagittal images, and one or morethird coronal images from the output image 903. The processing device140A may generate a concatenated image 904 by concatenating the firstaxial image(s), the second axial image(s), and the third axial image(s);a concatenated image 905 by concatenating by the first sagittalimage(s), the second sagittal image(s), and third sagittal image(s); anda concatenated image 906 by concatenating the first coronary image(s),the second coronary image(s), and the third coronary image(s). Theconcatenated images 904, 905, and 906 may serve as an input 910 of thetrained second model 907 as shown in FIG. 9. In some embodiments, thetrained second model 907 may have a same or similar configuration as thetrained model 800 as illustrated in FIG. 8. The concatenated images 904,905, and 906 may be processed by an axial view component, a sagittalview component, and a coronary view component of the trained secondmodel 907, respectively.

Similarly, for each of the other trained second model(s), the processingdevice 140A may generate an input of the trained second model based onan output image of a previous model connected to the trained secondmodel and optionally the input 901. An output image 909 of the lasttrained second model 908 may be the output of the trained cascadedneural network 900. For example, the output image 909 may be designatedas the target image. As another example, the output image 909 may bepost-processed by the processing device 140A to generate the targetimage.

According to some embodiments of the present disclosure, the input 901of the trained cascaded neural network 900 may be input into eachtrained second model in combination with an output image of a previoustrained model connected to the trained second model. In this way, eachtrained second model may extract features from the original input 901 aswell and the output of the previous trained model, which may avoid aloss of image data due to operation.

It should be noted that the example in FIG. 9 is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. The operations ofthe illustrated process presented above are intended to be illustrative.The trained cascaded neural network 900 may include one or moreadditional components and/or without one or more of the components shownin FIG. 9. For example, the trained cascaded neural network 900 mayinclude any count of trained second models.

FIG. 10 is a flowchart illustrating an exemplary process for generatingan image reconstruction model according to some embodiments of thepresent disclosure. In some embodiments, process 1000 may be implementedas a set of instructions (e.g., an application) stored in a storagedevice (e.g., the storage device 150, storage 220, and/or storage 390).The processing device 140B (e.g., the processor 210, the CPU 340, and/orone or more modules illustrated in FIG. 4B) may execute the set ofinstructions, and when executing the instructions, the processing device140B may be configured to perform process 1000. In some embodiments, theprocess 1000 may be performed by another device or system other than theimaging system 100, e.g., a device or system of a vendor of amanufacturer. For illustration purposes, the implementation of theprocess 1000 by the processing device 140B is described as an example.

In 1010, the processing device 140B (e.g., the acquisition module 404)may obtain a plurality of training samples. Each of the plurality oftraining samples may include a sample initial image of a sample subject,a sample gradient image associated with the sample initial image, and asample target image (or referred to as a ground truth target image) ofthe sample subject. The sample target image of each training sample mayhave a higher image quality than the sample initial image of thetraining sample.

For a training sample, the corresponding sample subject may be of thesame type as or a different type from the subject as described inconnection with 510. For example, the subject may be the heart of apatient, and the sample subject may be the heart of another patient. Thesample initial image refers to an image generated based on first scandata of the sample subject that is collected by a sample imaging device(e.g., a PET device, a PET/CT device) at a first instance. The sampletarget image refers to an image generated based on second scan data ofthe sample subject that is collected by the sample imaging device at asecond instance. The sample gradient image associated with the sampleinitial image refers to any image that includes detail information(e.g., edge information, texture information, color information) of thesample initial image.

In some embodiments, the first instance may correspond to a first set ofvalues of a parameter set (e.g., one or more scanning parameters of thesample imaging device), and the second instance may correspond to asecond set of values of the parameter. The first value of at least oneparameter (e.g., a radiation dose, a scan time, a patient movement) maybe different from the second value of the at least one parameter,wherein the value difference may induce the difference of the imagequality between the sample initial image and the sample target image ofthe training sample. Merely by way of example, the sample initial imageof the training sample may correspond to a first radiation doseassociated with the sample subject, and the sample target image maycorrespond to a second radiation dose associated with the samplesubject. The second radiation dose may be higher than the firstradiation dose. The sample target image corresponding to the secondradiation dose may have a higher image quality than the sample initialimage. More descriptions regarding the first and second radiation dosesmay be found elsewhere in the disclosure. See, e.g., FIG. 5 and relevantdescriptions thereof.

In some embodiments, the first radiation dose may be represented in theform of a first value, and the sample initial images of differenttraining samples may correspond to a same radiation dose having thefirst value. As another example, the first radiation dose may berepresented in the form of a first value range. The sample initialimages of different training samples may correspond to a same radiationdose or different radiation doses within the first value range.Similarly, the second radiation dose may be represented in the form of asecond value or a second value range. The sample target images ofdifferent training samples may correspond to a same radiation dosehaving the second value or within the second value range, or differentradiation doses within the second value range.

In some embodiments, at least a portion of the training sample may begenerated by the processing device 140B. For example, the processingdevice 140B may acquire the first and second scan data of the samplesubject from the sample imaging device or a storage device (e.g., thestorage 220, the storage 390, or an external source) that stores thefirst and second scan data. The processing device 140B may reconstructthe sample initial image based on the first scan data, and the sampletarget image based on the second scan data according to an imagereconstruction algorithm. The processing device 140B may furthergenerate the sample gradient image by processing the sample initialimage. The generation of the sample gradient image based on the sampleinitial image may be performed in a similar manner with that of thegradient image based on the initial image as described in connectionwith operation 520, and the descriptions thereof are not repeated here.

Optionally, the processing device 140B may further preprocess one ormore of the sample initial image, the sample target image, and thesample gradient image of the training sample to generate a preprocessedtraining sample. For example, the image reconstruction model maycorrespond to a target image resolution. If the image resolution of thesample initial image is different from the target image resolution, theprocessing device 140B may generate a resampled sample initial imagehaving the target image resolution. The processing device 140B mayfurther normalize the resampled sample initial image to generate apreprocessed sample initial image. As another example, if the imageresolution of the sample target image is different from the target imageresolution, the processing device 140B may generate a resampled sampletarget image having the target image resolution. The processing device140B may further normalize the resampled sample target image to generatea preprocessed sample target image. As yet another example, theprocessing device 140B may generate a preprocessed gradient image bynormalizing the sample gradient image.

In some alternative embodiments, the training sample (or a portionthereof) or the preprocessed training sample (or a portion thereof) maybe previously generated by a computing device (e.g., the processingdevice 140B) and stored in a storage device (e.g., the storage device150, the storage 220, the storage 390, or an external database). Theprocessing device 140B may retrieve the training sample (or a portionthereof) or the preprocessed training sample (or a portion thereof)directly from the storage device. For illustration purposes, thefollowing descriptions are described with reference to generating theimage reconstruction model based on the training samples. This is notintended to be limiting, and the image reconstruction model may begenerated based on the preprocessed training samples according to someother embodiments of the present disclosure.

In 1020, the processing device 140B (e.g., the acquisition module 404)may obtain a preliminary model.

The preliminary model may be of any type of neural network model, forexample, a neural network model (e.g., a CNN model, a GAN model, acascaded neural network, or the like). In some embodiment, thepreliminary model may be a cascaded neural network model including afirst model and one or more second models downstream to the first model.For example, the preliminary model may be trained to generate thetrained cascaded neural network 900 as shown in FIG. 9, wherein thefirst model may be trained to generate the trained first model 902, andthe one or more second models may be trained to generate the trainedsecond models 907, 908, or the like. In some embodiments, the processingdevice 140B may extract multi-view information (e.g., sample first 2Dimage(s) and sample second 2D image(s)) from each training sample. Thepreliminary model may include multiple components configured to processthe multi-view information of each training sample. More descriptionsregarding the processing of the multi-view information of each trainingsample may be found elsewhere in the present disclosure. See, e.g., FIG.11 and relevant descriptions thereof.

In some embodiments, the preliminary model may include one or more modelparameters. For example, the preliminary model may be a CNN model andexemplary model parameters of the preliminary model may include thenumber (or count) of layers, the number (or count) of kernels, a kernelsize, a stride, a padding of each convolutional layer, a loss function,or the like, or any combination thereof. Before training, the modelparameter(s) may have their respective initial values. For example, theprocessing device 140B may initialize parameter value(s) of the modelparameter(s) of the preliminary model.

In 1030, the processing device 140B (e.g., the model generation module405) may generate the image reconstruction model by training thepreliminary model using the plurality of training samples.

In some embodiments, the preliminary model may be trained according to amachine learning algorithm as described elsewhere in this disclosure(e.g., FIG. 5 and the relevant descriptions). For example, theprocessing device 140B may generate the image reconstruction modelaccording to a supervised machine learning algorithm by performing oneor more iterations to iteratively update the model parameter(s) of thepreliminary model. For illustration purposes, an exemplary currentiteration of the iteration(s) is described in the following description.The current iteration may be performed based on at least a portion ofthe training samples. In some embodiments, a same set or different setsof training samples may be used in different iterations in training thepreliminary model.

In the current iteration, for each of at least a portion of theplurality of training samples, the processing device 140B may generate apredicted target image of the corresponding training subject byinputting a training input into an updated preliminary model determinedin a previous iteration. For example, the training input of the updatedpreliminary model may include the sample initial image and the samplegradient image of each training sample. As another example, the traininginput of the updated preliminary model may include one or more samplefirst 2D images and one or more sample second 2D images of each trainingsample (which will be described in detail in connection with FIG. 11).

The processing device 140B may then determine a value of a loss functionof the updated preliminary model based on the predicted target image andthe sample target image of each of the at least a portion of theplurality of training samples. The loss function may be used to evaluatethe accuracy and reliability of the updated preliminary model, forexample, the smaller the loss function is, the more reliable the updatedpreliminary model is. Exemplary loss functions may include an L1 lossfunction, a focal loss function, a log loss function, a cross-entropyloss function, a Dice loss function, etc. The processing device 140B mayfurther update the value(s) of the model parameter(s) of the updatedpreliminary model to be used in a next iteration based on the value ofthe loss function according to, for example, a backpropagationalgorithm.

In some embodiments, the one or more iterations may be terminated if atermination condition is satisfied in the current iteration. Anexemplary termination condition may be that the value of the lossfunction obtained in the current iteration is less than a predeterminedthreshold. Other exemplary termination conditions may include that acertain count of iterations is performed, that the loss functionconverges such that the differences of the values of the loss functionobtained in consecutive iterations are within a threshold, etc. If thetermination condition is satisfied in the current iteration, theprocessing device 140B may designate the updated preliminary model asthe image reconstruction model.

In some embodiments, the processing device 140B may adopt a specificlearning rate schedule in training the preliminary model. For example,an adaptive learning rate schedule, such as a time-based decay schedule,a step decay schedule, or an exponential decay schedule, may be adopted.Merely by way of example, an initial learning rate of the preliminarymodel may be equal to 0.0001, and dropped by half every 10,000 epochs inmodel training, which may facilitate a convergence of the preliminarymodel. Additionally or alternatively, the processing device 140B mayadopt a specific technique, such as a drop-out algorithm, a weight-decayalgorithm, in training the preliminary model to prevent the preliminarymodel from overfitting.

In some alternative embodiments, each training sample may merely includea sample initial image and a sample target image. The preliminary modelmay be trained to learn detail information of the sample initial imageof each training sample. For example, the loss function of thepreliminary model may incorporate a perceptual loss and/or a gradientloss. However, in such cases, with the increase of the depth of thepreliminary model, the amount of the detail information may increase,which may result in overfitting. Compared with learning the detailinformation in model training, using a training sample that includes asample gradient image may reduce the computational complexity and/orcost, and avoid overfitting.

In some embodiments, the processing device 140B may perform one or moreoperations in process 1100 as shown in FIG. 11 to train the preliminarymodel using the plurality of training samples.

In 1110, for each training sample, the processing device 140B (e.g., themodel generation module 405) may extract one or more sample first 2Dimages from the sample initial image of the training sample. Forexample, the sample first 2D image(s) of a training sample may includeone or more sample first axial images, one or more sample first sagittalimages, and one or more sample first coronal images extracted from thecorresponding sample initial image. The extraction of the sample first2D image(s) from a sample initial image may be performed in a similarmanner with that of the first 2D image(s) from the initial image asdescribed in connection with operation 610, and the descriptions thereofare not repeated here.

In 1120, for each training sample, the processing device 140B (e.g., themodel generation module 405) may extract one or more sample second 2Dimages from the sample gradient image of the training sample. Forexample, the sample second 2D image(s) of a training sample may includeone or more sample second axial images, one or more sample secondsagittal images, and one or more sample second coronal images extractedfrom the corresponding sample gradient image. The extraction of thesample second 2D image(s) from a sample gradient image may be performedin a similar manner with that of the second 2D image(s) from thegradient image as described in connection with operation 620, and thedescriptions thereof are not repeated here.

In 1130, the processing device 140B may generate the imagereconstruction model by training the preliminary model using the samplefirst 2D image(s), the sample second 2D image(s), and the sample targetimage of each of the plurality of training samples.

For example, for each training sample, the processing device 140B mayconcatenate the sample first 2D image(s) and the sample second 2Dimage(s) of the training sample into one or more sample concatenatedimages. Merely by way of example, the processing device 140B maygenerate a sample first concatenated image by concatenating the samplefirst axial image(s) and the sample second axial image(s) of thetraining sample; a sample second concatenated image by concatenating thesample first sagittal image(s) and the sample second sagittal image(s);and a sample third concatenated image by concatenating the sample firstcoronary image(s) and the sample second coronary image(s). In someembodiments, the concatenation of the sample first 2D image(s) and thesample second 2D image(s) may be performed in a similar manner with theconcentration of the first 2D image(s) and the second 2D image(s) asdescribed in the operation 630, and the descriptions thereof are notrepeated here.

The processing device 140B may further train the preliminary model usingthe sample concatenated image(s) and the sample target image of eachtraining sample. For example, in an iterative training process of thepreliminary model, the sample concatenated image(s) of each trainingsample may serve as a training input of the updated preliminary modeldetermined in a previous iteration to generate a predicted target imageof the training sample. The predicted target image and the sample targetimage of each training sample may then be used to determine the value ofthe loss function and further update the updated preliminary model.

In some embodiments, the preliminary model may include a firstcomponent, a second component, a third component, and a fourthcomponent. For a training sample, the first component may be configuredto generate a sample first feature map by processing the sample firstconcatenated image of the training sample. The second component may beconfigured to generate a sample second feature map by processing thesample second concatenated image of the training sample. The thirdcomponent may be configured to generate a sample third feature map byprocessing the sample third concatenated image of the training sample.The fourth component may be configured to process the sample firstfeature map, the sample second feature map, and the sample third featuremap of the training sample. In some embodiments, the first, second,third, and fourth components may be trained to generate the axial viewcomponent 810, the sagittal view component 820, the coronary viewcomponent 830, and the integration component 840 of the trained model800 as shown in FIG. 8, respectively.

In some embodiments, the preliminary model may be a cascaded neuralnetwork that includes a plurality of sequentially connected models. Themodels may include a first model and one or more second modelsdownstream to the first model. The processing device 140B may performprocess 1200 as shown in FIG. 12 to sequentially train the first modeland the second model(s) using the training samples, so as to generatethe image reconstruction model. Optionally, each of the models of thecascaded neural network may include the first, second, third, and fourthcomponents as aforementioned.

In 1210, the processing device 140B (e.g., the model generation module405) may train the first model using the sample first 2D image(s), thesample second 2D image(s), and the sample target image of each of theplurality of training samples.

In some embodiments, the training of the first model using the samplefirst 2D image(s), the sample second 2D image(s), and the sample targetimage of each training sample may be performed in a similar manner astraining of the preliminary model using the sample first 2D image(s),the sample second 2D image(s), and the sample target image of eachtraining sample as described in connection with operation 1130. Forexample, the processing device 140B may train the first model byiteratively updating value(s) of one or more model parameters of thefirst mode according to the sample first 2D image(s), the sample second2D image(s), and the sample target image of each training sample.

After the trained first model is generated, the processing device 140Bmay sequentially train the second model(s) according to a deepauto-context learning strategy. For example, for each second model,operations 1220 to 1240 may be performed. After a specific second modelis trained, the training of a next second model that is downstream andconnected to the specific second model may be performed. Forillustration purposes, an implementation of operations 1220-1240 for oneof the second model(s) is described hereinafter.

In 1220, for each training sample, the processing device 140B (e.g., themodel generation module 405) may obtain a sample output image byinputting the sample first 2D image(s) and the sample second 2D image(s)of the training sample into the one or more trained models that aregenerated before the training of the second model.

For example, the cascaded neural network may include a first model(denoted as P1) and q second models (denoted as Q1 to Qq). For an j^(th)second model, the one or more trained models generated before thetraining of the j^(th) second model may include the trained P1, thetrained Q1, the trained Q2, . . . , and the trained Q(q−1). In someembodiments, for a training sample, the processing device 140B maydirectly input the sample first 2D image(s) and the sample second 2Dimage(s) into the trained model(s) to obtain the sample output image.Alternatively, the processing device 140B may input the sampleconcatenated image(s) of the sample first 2D image(s) and the samplesecond 2D image(s) into the trained model(s) to obtain the sample outputimage.

In 1230, for each training sample, the processing device 140B (e.g., themodel generation module 405) may extract one or more sample third 2Dimages from the sample output image corresponding to the trainingsample.

The sample output image of a training sample obtained in 1220 may be a3D image, and a sample third 2D image may be extracted from the sampleoutput image along any direction. For example, the sample third 2Dimage(s) may include one or more sample third axial images, one or moresample third sagittal images, and one or more sample third coronalimages extracted from the sample output image. In some embodiments,extraction of the sample third 2D image(s) from a sample output imagemay be performed in a similar manner as that of the third 2D image(s)from the output image as described in connection with operation 720.

In 1240, the processing device 140B (e.g., the model generation module405) may train the second model using the sample first 2D image(s), thesample second 2D image(s), the sample third 2D image(s), and the sampletarget image of each training sample.

In some embodiments, the training of the second model may include one ormore second iterations to iteratively update value(s) of modelparameter(s) of the second model. For example, in a current seconditeration, the processing device 140B may input the sample first 2Dimage(s), the sample second 2D image(s), the sample third 2D image(s) ofeach training sample into the updated second model determined in aprevious second iteration to generate a second predicted target image ofthe training sample. As another example, for each training sample, theprocessing device 140B may generate a sample fifth concatenated image byconcatenating the sample first axial image(s), the sample second axialimage(s), and the sample third axial image(s); a sample sixthconcatenated image by concatenating the sample first sagittal image(s),the sample second sagittal image(s), and the sample third sagittalimage(s); and a sample seventh concatenated image by concatenating thesample first coronary image(s), the sample second coronary image(s), andthe sample third coronary image(s). The processing device 140B mayfurther input the sample fifth, sixth, and seventh concatenated imagesinto the updated second model to obtain the second predicted targetimage. The processing device 140B may determine a value of a second lossfunction based on the second predicted target image and the sampletarget image of each training sample. The processing device 140B mayfurther update the updated second model based on the value of the secondloss function until a second termination condition is satisfied. Thesecond termination condition may be similar to the termination conditionas described in connection with FIG. 10.

According to some embodiments of the present disclosure, the cascadedneural network may be trained using a deep auto-context learningstrategy. By using the deep auto-context learning strategy, the traininginput may be inputted into each of the models during the trainingprocess. This may avoid a loss of image data due to operations (e.g., aconvolutional operation) performed in training. In addition, adifference between a sample output image of each model and the sampletarget image of each training sample may be reduced gradually, therebygenerating an image reconstruction model with improved accuracy andreliability.

In some alternative embodiments, the models of the cascaded neuralnetwork may be trained simultaneously. For example, in an iterativetraining process of the cascaded neural network, the processing device140B may input the sample first 2D image(s) and the sample second 2Dimage(s) of each training sample into the cascaded neural network. Anoutput image of the last second model of the cascaded neural network maybe designated as a predicted target image of each training sample. Theprocessing device 140B may further jointly update model parameter(s) ofeach model of the cascaded neural network based on the predicted targetimage and the sample target image of each training sample. Additionallyor alternatively, the cascaded neural network may be trained using theoriginal training samples (e.g., the sample initial image, the samplegradient image, and the sample target image of each training sample). Insome embodiments, different models of the cascaded neural network may betrained using a same set or different sets of training samples.

It should be noted that the above descriptions regarding the process1000 to 1200 is merely provided for the purposes of illustration, andnot intended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations and modificationsmay be made under the teachings of the present disclosure. However,those variations and modifications do not depart from the scope of thepresent disclosure. In some embodiments, the order of the process 1000,1100, and/or 1200 is not intended to be limiting. For example, theoperation 1020 may be performed before operation 1010 or operations 1010and 1020 may be performed simultaneously.

In some embodiments, one or more operations may be added or omitted. Forexample, after the image reconstruction model is generated, theprocessing device 140B may further test the image reconstruction modelusing a set of testing samples. Additionally or alternatively, theprocessing device 140B may update the image reconstruction modelperiodically or irregularly based on one or more newly-generatedtraining images (e.g., new sample initial images generated in medicaldiagnosis).

In some embodiments, the processing device 140B may extract one or moreimage patches from one or more images as aforementioned (e.g., a sampleinitial image, a sample gradient image, a sample first 2D image, asample second 2D image, a sample concatenated image, a sample outputimage), and use the image patches in the training process of thepreliminary model. Merely by way of example, a plurality of first imagepatches each having a size of 64*64*10 may be extracted from the sampleinitial image and the sample gradient image of each training sample, andbe used in training the preliminary model. As another example, aplurality of second image patches each having a size of 64*64*1 may beextracted from the sample first 2D image(s) and the sample second 2Dimage(s) of each training sample. The first and second sampleconcatenated images of each training sample may be generated based onthe second image patches.

FIG. 13A is a schematic diagram illustrating an exemplary sample initialimage 1300A according to some embodiments of the present disclosure.FIG. 13B is a schematic diagram illustrating an exemplary predictedtarget image 1300B according to some embodiments of the presentdisclosure. FIG. 13C is a schematic diagram illustrating an exemplarysample target image 1300C according to some embodiments of the presentdisclosure. The sample initial image 1300A and the sample target image1300C are PET images of a first patient. The sample initial image 1300Awas reconstructed based on image data collected by a PET/CT scanner andcorresponds to a first radiation dose. The sample target image 1300C wasreconstructed based on image data collected by the PET/CT scanner andcorresponds to a second radiation dose higher than the first radiationdose. The predicted target image 1300B was generated based on the sampleinitial image 1300A by applying an image reconstruction model asdescribed elsewhere in this disclosure (e.g., FIG. 5 and the relevantdescriptions). The predicted target image 1300B corresponds to thesecond radiation dose. As shown in FIGS. 13A to 13C, the predictedtarget image 1300B and the sample target image 1300C have a lower noiselevel than the sample initial image 1300A. The standardized uptake value(SUV) of the predicted target image 1300B is close to that of the sampletarget image 1300C.

FIG. 14A is a schematic diagram illustrating an exemplary sample initialimage 1400A according to some embodiments of the present disclosure.FIG. 14B is a schematic diagram illustrating an exemplary predictedtarget image 1400B according to some embodiments of the presentdisclosure. FIG. 14C is a schematic diagram illustrating an exemplarysample target image 1400C according to some embodiments of the presentdisclosure. The sample initial image 1400A and the sample target image1400C are PET images of a second patient. The sample initial image 1400Awas reconstructed based on image data collected by a PET/CT scanner andcorresponds to the first radiation dose. The sample target image 1400Cwas reconstructed based on image data collected by the PET/CT scannerand corresponds to the second radiation dose. The predicted target image1400B was generated based on the sample initial image 1400A by applyingan image reconstruction model as described elsewhere in this disclosure(e.g., FIG. 5 and the relevant descriptions). The predicted target image1400B corresponds to the second radiation dose. As shown in FIGS. 14A to14C, the predicted target image 1400B and the sample target image 1400Chave a lower noise level than the sample initial image 1400A. The SUV ofthe predicted target image 1400B is close to that of the sample targetimage 1400C.

FIG. 15A is a schematic diagram illustrating an exemplary sample initialimage 1500A according to some embodiments of the present disclosure.FIG. 15B is a schematic diagram illustrating an exemplary predictedtarget image 1500B according to some embodiments of the presentdisclosure. FIG. 15C is a schematic diagram illustrating an exemplarysample target image 1500C according to some embodiments of the presentdisclosure. The sample initial image 1500A and the sample target image1500C are PET images of a third patient. The sample initial image 1500Awas reconstructed based on image data collected by a PET/CT scanner andcorresponds to the first radiation dose. The sample target image 1500Cwas reconstructed based on image data collected by the PET/CT scannerand corresponds to the second radiation dose. The predicted target image1500B was generated based on the sample initial image 1500A by applyingan image reconstruction model as described elsewhere in this disclosure(e.g., FIG. 5 and the relevant descriptions). The predicted target image1500B corresponds to the second radiation dose. As shown in FIGS. 15A to15C, the predicted target image 1500B and the sample target image 1500Chave a lower noise level than the sample initial image 1500A. The SUV ofthe predicted target image 1500B is close to that of the sample targetimage 1500C.

According to FIGS. 13A to 15C, the systems and methods disclosed hereinmay be used to reconstruct PET images corresponding to the secondradiation dose without increasing radiation damage and withoutincreasing the scan time.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer-readable media having computer-readableprogram code embodied thereon.

A non-transitory computer-readable signal medium may include apropagated data signal with computer readable program code embodiedtherein, for example, in baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, includingelectromagnetic, optical, or the like, or any suitable combinationthereof. A computer-readable signal medium may be any computer-readablemedium that is not a computer-readable storage medium and that maycommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Program code embodied on a computer-readable signal medium may betransmitted using any appropriate medium, including wireless, wireline,optical fiber cable, RF, or the like, or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C #, VB.NET, Python or the like, conventional procedural programming languages,such as the “C” programming language, Visual Basic, Fortran, Perl,COBOL, PHP, ABAP, dynamic programming languages such as Python, Ruby,and Groovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations, therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose and that the appended claimsare not limited to the disclosed embodiments, but, on the contrary, areintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the disclosed embodiments. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as asoftware-only solution, e.g., an installation on an existing server ormobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereofto streamline the disclosure aiding in the understanding of one or moreof the various inventive embodiments. This method of disclosure,however, is not to be interpreted as reflecting an intention that theclaimed object matter requires more features than are expressly recitedin each claim. Rather, inventive embodiments lie in less than allfeatures of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, properties, andso forth, used to describe and claim certain embodiments of theapplication are to be understood as being modified in some instances bythe term “about,” “approximate,” or “substantially.” For example,“about,” “approximate” or “substantially” may indicate ±20% variation ofthe value it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting effect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A system for image reconstruction, comprising: atleast one storage device including a set of instructions; and at leastone processor configured to communicate with the at least one storagedevice, wherein when executing the set of instructions, the at least oneprocessor is configured to direct the system to perform operationsincluding: obtaining an initial image of a subject, the initial imagebeing generated based on scan data of the subject that is collected byan imaging device; generating a gradient image associated with theinitial image; determining a model input based on the initial image andthe gradient image; and generating a target image of the subject byinputting the model input into an image reconstruction model, the targetimage having a higher image quality than the initial image.
 2. Thesystem of claim 1, wherein each of the initial image and the gradientimage is a 3-dimensional (3D) image, and the determining a model inputbased on the initial image and the gradient image includes: extractingat least one first 2-dimensional (2D) image from the initial image;extracting at least one second 2D image from the gradient image; anddetermining, based on the at least one first 2D image and the at leastone second 2D image, the model input.
 3. The system of claim 2, wherein:the at least one first 2D image includes at least one first axial image,at least one first sagittal image, and at least one first coronary imageextracted from the initial image; and the at least one second 2D imageincludes at least one second axial image, at least one second sagittalimage, and at least one second coronary image extracted from thegradient image.
 4. The system of claim 3, wherein the determining, basedon the at least one first 2D image and the at least one second 2D image,the model input includes: generating a first concatenated image byconcatenating the at least one first axial image and the at least onesecond axial image; generating a second concatenated image byconcatenating the at least one first sagittal image and the at least onesecond sagittal image; generating a third concatenated image byconcatenating the at least one first coronary image and the at least onesecond coronary image; and determining the model input based on thefirst concatenated image, the second concatenated image, and the thirdconcatenated image.
 5. The system of claim 4, wherein the imagereconstruction model includes: an axial view component configured togenerate a first feature map by processing the first concatenated image;a sagittal view component configured to generate a second feature map byprocessing the second concatenated image; a coronary view componentconfigured to generate a third feature map by processing the thirdconcatenated image; and an integration component configured to generatean output image by processing the first feature map, the second featuremap, and the third feature map, wherein the target image is generatedbased on the output image of the integration component.
 6. The system ofclaim 2, wherein the image reconstruction model is a trained cascadedneural network including a plurality of trained models that aresequentially connected, the plurality of trained models include atrained first model and one or more trained second models downstream tothe trained first model, and the generating the target image by applyingthe image reconstruction model on the at least one first 2D image andthe at least one second 2D image includes: obtaining an output image ofthe trained first model by inputting the at least one first 2D image andthe at least one second 2D image into the trained first model; for eachof the one or more trained second models, extracting at least one third2D image from an output image of a previous trained model connected tothe trained second model; and obtaining an output image of the trainedsecond model by inputting the at least one first 2D image, the at leastone second 2D image, and the at least one third 2D image into thetrained second model, wherein the target image is generated based on anoutput image of the last trained second model of the trained cascadedneural network.
 7. The system of claim 1, wherein the scan data of theinitial image corresponds to a first radiation dose associated with thesubject, and the target image corresponds to a second radiation dosehigher than the first radiation dose.
 8. The system of claim 1, whereinthe image reconstruction model corresponds to a target image resolution,the initial image has an image resolution different from the targetimage resolution, and the at least one processor is further configuredto direct the system to perform the operations including: generating aresampled initial image having the target image resolution by resamplingthe initial image; generating a preprocessed initial image bynormalizing the resampled initial image; and generating a preprocessedgradient image by normalizing the gradient image, and wherein thedetermining a model input based on the initial image and the gradientimage includes: determining, based on the preprocessed initial image andthe preprocessed gradient image, the model input.
 9. The system of claim1, wherein the image quality relates to at least one of an imageresolution, a noise level, a contrast ratio, or a sharpness.
 10. Thesystem of claim 1, wherein the image reconstruction model is furtherconfigured to reduce noise in the initial image.
 11. The system of claim1, wherein the image reconstruction model includes a neural networkmodel.
 12. A method for image reconstruction implemented on a computingdevice having at least one processor and at least one storage device,the method comprising: obtaining an initial image of a subject, theinitial image being generated based on scan data of the subject that iscollected by an imaging device; generating a gradient image associatedwith the initial image; determining a model input based on the initialimage and the gradient image; and generating a target image of thesubject by inputting the model input into an image reconstruction model,the target image having a higher image quality than the initial image.13. The method of claim 12, wherein each of the initial image and thegradient image is a 3-dimensional (3D) image, and the determining amodel input based on the initial image and the gradient image includes:extracting at least one first 2-dimensional (2D) image from the initialimage; extracting at least one second 2D image from the gradient image;and determining, based on the at least one first 2D image and the atleast one second 2D image, the model input.
 14. The method of claim 13,wherein: the at least one first 2D image includes at least one firstaxial image, at least one first sagittal image, and at least one firstcoronary image extracted from the initial image; and the at least onesecond 2D image includes at least one second axial image, at least onesecond sagittal image, and at least one second coronary image extractedfrom the gradient image.
 15. The method of claim 14, wherein thedetermining, based on the at least one first 2D image and the at leastone second 2D image, the model input includes: generating a firstconcatenated image by concatenating the at least one first axial imageand the at least one second axial image; generating a secondconcatenated image by concatenating the at least one first sagittalimage and the at least one second sagittal image; generating a thirdconcatenated image by concatenating the at least one first coronaryimage and the at least one second coronary image; and determining themodel input based on the first concatenated image, the secondconcatenated image, and the third concatenated image.
 16. The method ofclaim 15, wherein the image reconstruction model includes: an axial viewcomponent configured to generate a first feature map by processing thefirst concatenated image; a sagittal view component configured togenerate a second feature map by processing the second concatenatedimage; a coronary view component configured to generate a third featuremap by processing the third concatenated image; and an integrationcomponent configured to generate an output image by processing the firstfeature map, the second feature map, and the third feature map, whereinthe target image is generated based on the output image of theintegration component.
 17. The method of claim 13, wherein the imagereconstruction model is a trained cascaded neural network including aplurality of trained models that are sequentially connected, theplurality of trained models include a trained first model and one ormore trained second models downstream to the trained first model, andthe generating the target image by applying the image reconstructionmodel on the at least one first 2D image and the at least one second 2Dimage includes: obtaining an output image of the trained first model byinputting the at least one first 2D image and the at least one second 2Dimage into the trained first model; for each of the one or more trainedsecond models, extracting at least one third 2D image from an outputimage of a previous trained model connected to the trained second model;and obtaining an output image of the trained second model by inputtingthe at least one first 2D image, the at least one second 2D image, andthe at least one third 2D image into the trained second model, whereinthe target image is generated based on an output image of the lasttrained second model of the trained cascaded neural network.
 18. Themethod of claim 12, wherein the scan data of the initial imagecorresponds to a first radiation dose associated with the subject, andthe target image corresponds to a second radiation dose higher than thefirst radiation dose.
 19. The method of claim 12, wherein the imagereconstruction model corresponds to a target image resolution, theinitial image has an image resolution different from the target imageresolution, and the method further includes: generating a resampledinitial image having the target image resolution by resampling theinitial image; generating a preprocessed initial image by normalizingthe resampled initial image; and generating a preprocessed gradientimage by normalizing the gradient image, and wherein the determining amodel input based on the initial image and the gradient image includes:determining, based on the preprocessed initial image and thepreprocessed gradient image, the model input.
 20. The method of claim12, wherein the image quality relates to at least one of an imageresolution, a noise level, a contrast ratio, or a sharpness.